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Accepted Manuscript Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy

Marco Beccali, Giuseppina Ciulla, Valerio Lo Brano, Alessandra Galatioto, Marina Bonomolo PII:

S0360-5442(17)31018-6

DOI:

10.1016/j.energy.2017.05.200

Reference:

EGY 11032

To appear in:

Energy

Received Date:

02 November 2016

Revised Date:

05 May 2017

Accepted Date:

31 May 2017

Please cite this article as: Marco Beccali, Giuseppina Ciulla, Valerio Lo Brano, Alessandra Galatioto, Marina Bonomolo, Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy, Energy (2017), doi: 10.1016/j.energy.2017.05.200

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ACCEPTED MANUSCRIPT Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy Marco Beccali1, Giuseppina Ciulla*1, Valerio Lo Brano1, Alessandra Galatioto1, Marina Bonomolo1 1 Dipartimento

dell’Energia, Ingegneria dell’informazione e dei Modelli matematici, Scuola Politecnica- Università di Palermo, Viale delle Scienze Edificio 9, 90128 Palermo, Italy *corresponding author: [email protected]

ABSTRACT The public buildings sector represents one of the most intensive items of EU energy consumption; the application of retrofit solutions in existing buildings is a crucial way to reduce its impact. To facilitate the knowledge of the energy performance of existing non-residential buildings and the choice of the more adequate actions, Public Administrations (PA) should have the availability of proper tools. Within the Italian project “POI 2007-13”, a database and a decision support tool, for easy use, even to a non-technical user, have been developed. A large set of data, obtained from the energy audits of 151 existing public buildings located in four regions of South Italy have been analysed, elaborated, and organised in a database. This was used to identify the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. A decision support tool, based on the use of these ANNs is conceived for a fast prediction of the energy performance of buildings and for a first selection of energy retrofit actions that can be applied. KEYWORDS Energy efficiency, retrofit action, ANN, energy audit, decision support tool, non-residential building. NOMENCLATURE Ai AN ANN ATC AEC bi Eff Fe Fh G h HDD IENH IENE

Activation potential of an artificial neuron Artificial Neuron Artificial Neural Network Average Thermal Consumption Average Electrical Consumption “Bias” for the neuron Effectiveness Normalization factor depending on building use and S/V Normalization factor according to plants operating hours Monthly horizontal solar irradiance; Number of operating hours of the plants in the heating season; Heating Degree Days. Normalized Energy Index for Heating Index Normalized Energy Index for Electrical consumption 1

ACCEPTED MANUSCRIPT N number of elements in the input vector PE Primary energy demand S/V Shape factor Sglass Glazed Surface Sopaque Opaque Surface Suseful Useful Surface Uglass Average thermal transmittance of glass surface Uopaque Average thermal transmittance of opaque surface Vgross Gross heated Volume wij weights of the ANN xi neuron input yi neuron output  activate on function g Global efficiency of the heating plant

1. INTRODUCTION In order to achieve the goals of Europe 2020, many energy saving actions have to be adopted by Member States [1]. In this framework, the public buildings sector is responsible for a large part of European Union energy consumption [2, 3]. Its average yearly specific primary energy demand is very high, about 220 kWh/m2y [4]. Thus, effective actions for its reduction conducted by public administrations are necessary in order to reduce greenhouse gas emissions and CO2-decommissioning of the European economy. Today, existing energy saving regulations are strongly restrictive on new buildings, meanwhile the international debate is focused on the definition and application of Net Zero Energy Building targeting existing buildings [5-7] as a new worldwide challenge [8]. The escalation of energy costs and impacts of energy consumption on the environment have compelled government agencies and researchers to develop tools and retrofit measures to conserve energy in existing buildings. On the demand-side, a multi-country effort under the International Energy Agency has led to the gathering, evaluation and documentation of the largest collection of energy retrofit measures for commercial, residential and industrial buildings [9]. Since the public building sector, including schools, have a key role in relation to energy saving for the whole community, they are considered as one of the starting points for energy efficiency. In addition to energy consumption reduction obligatory in new constructions, it is broadly accepted that a drastic decrease in energy consumption is needed in existing educational buildings. A large scientific literature investigates strategies for energy efficient building retrofits. Series of procedures have been used in different studies, according to the local climate and the construction style [10]. Concerning Italy, the Istitituto Nazionale di Statistica (ISTAT) has estimated that the current school building stock amounts to 50,157 national schools with 49% nursery schools, 35% primary schools, and 16% first-level secondary schools [11]. Generally, Italian public building stock is characterised by inadequate envelopes and low performance HVAC systems. A detailed analysis among geographical areas is necessary to understand where to focus maintenance and refurbishment actions and their relative funding. In other words, a detailed knowledge about the state-of-the-art is a useful approach to help local PA in defining and adopting not only energy saving actions for new constructions but also effective and retrofit measures for the current estate [12-14]. In literature, various modelling techniques for estimating the energy consumption of buildings have been developed. Some predictive tools use recorded and/or generated energy consumption 2

ACCEPTED MANUSCRIPT data along with statistical methods such as regression methods, artificial neural networks (ANN), or decision trees, to forecast the energy consumption of building [15, 16]. Others, including EnergyPlus, DOE and TRNSYS, use more fundamental approaches such as the mass and heat balance technique to simulate the building thermal loads. Currently, in Italy many local and international research teams are involved in collecting data of energy consumption of the public building stock, aiming to identify the best energy retrofit actions and to reduce national CO2 emissions. In this paper, the authors describes a methodology to determine, for local PA, a decision support tool on the energy efficiency evaluation of an existing building and the selection of the best energy efficiency solutions. After an accurate energy audit of the existing school-buildings stock it was possible to develop an accurate energy database that represent the base of the training of a specific ANN and the development of a decision support tool. 2. METHOD This paper resumes the results of a study conducted in the framework of a research programme “POI ENERGIA 2014-2020” belonging to EU Horizon 2020. The programme aims to characterise buildings that are managed by the Province authorities in terms of their energy performance and thus to elaborate actions for reducing consumption while improving their quality [17-19]. The programme was funded by the Italian Ministry of Economic Development (MISE), Italian Ministry of the Environment (MATTM) and the Unione delle Province d’Italia (UPI). The project was focused on the four Italian regions of the EU Convergence Objective (Campania, Puglia, Calabria, and Sicilia) [20]. The core of the project was to realise detailed energy audits of all the buildings belonging to UPI affiliated Provinces. This has allowed characterisation of the actual building stocks including thermophysical properties of the envelope, thermal system typologies, lighting and appliances and energy consumption accounted in utilities invoices. Generally, a more detailed analysis of the energy balance of a plant-building system should be obtained by a detailed simulation model that describes the energy behaviour of a building (with or without plant) in its climatic context. These kinds of models are hard to realise and onerous from the point of view of computing time. An alternative approach to link building data to its energy performance is the use of evolutionary algorithms, such as artificial neural networks (ANN), which are not based on the detailed knowledge of the dynamics of the system, but on the analysis of a large data set of input and output values. One of the main attractions of this methodology lies in the ability to represent a proper relationship between input and output, which in another way would be difficult, or sometimes impossible, to find an analytical relationship. ANNs “learn” to recognise relationships through a “training process” based on the use of the available empirical data, within the implicit assumption that data keep a non-explicit relationship (data-driven approach) [21-25]. This kind of model is very useful whenever the studied phenomenon is characterised by considerable complexity and interdependence of many factors. In order to evaluate the energy performance of a building as an output of such a model several input must be considered: the thermophysical parameters of the envelope, the HVAC plants typology and characteristics, the climate parameters, the building geometry, the use of indoor space [26], the shape factor (S/V), and the energy consumption. A preliminary statistical analysis of the energy audit of 151 buildings was permitted to characterise the actual building stock according to the above mentioned characteristics. In this way, it was possible to build a database, which can be assumed representative of the South Italy non-residential building stock [27, 28]. Furthermore, information related to the possible application of some retrofit action was organised, underlining the achievable energy performance, the economic feasibility and the payback time. This database was used to identify 3

ACCEPTED MANUSCRIPT the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. In this way, it was possible to develop a decision support tool able to identify the energy performance of typical South Italy non-residential buildings and to highlight the priorities among retrofit actions with their corresponding economic feasibility. The flow-chart in Fig. 1 shows the main thematic areas of the overall analysis and the parameters related to climate, building, HVAC system, and thermal and electrical consumption, which have been included in the database. This analysis was completed with the energy demand for several end-uses (space heating and DHW production) including electrical consumption (lighting system, cooling system, ventilation system, appliances). Three specific indexes have been calculated: the Normalized Energy Index for Heating (IENH), the Normalized Energy Index for Electrical consumption (IENE) and the Primary Energy Demand for space heating (PE). Furthermore, possible retrofit actions accompanied by a brief economic analysis, with correlated payback times, have been included in the database. Energy audits of this set of public buildings have been studied, in order to build a useful database of the “status quo” of the building stock. Information about thermophysical characteristics, HVAC performances and energy consumption was analysed. Table 1 shows the distribution of analysed buildings per region. These energy audits have been developed in four steps starting with a general view of the building, narrowing to a detailed study of retrofit actions, and their effect on the energy and economic balance. As an example, U-Values and thermo-hygrometric behaviour of each building component have been calculated and verified according to European Standards UNI EN ISO 6946:2008 and UNI EN ISO 13788:2003 [29, 30]. A detailed energy analysis was performed for several end-uses and corresponding energy carriers. 2.1 Climate context and Weather Data Italy is subdivided into six Climate Zones, from A (up to 600 Heating Degree Days (HDD)) to F (over 3000 HDD) [31, 32]. Conventional time extensions of heating seasons are also defined by Italian law [33]. Up to today, cooling season limits as well as “Cooling Degree Days (CDD)” have not been defined by Italian laws; hence, the calculation of conventional cooling demand is only based on thermal energy performance of the building envelope [34]. As reported by Citterio [35], about 27% of Italian public non-residential buildings are located in South Italy. Furthermore, 51% of them fall in Climate Zone C, 25% fall in Climate Zone D, and 16% in Climate Zone B. The four regions to which the analysed buildings belong are located in the following Climatic Zones: 1. Calabria: from 889 to 2693 HDD, (from B to E Zones); 2. Campania: from 994 to 2651 HDD, (from C to E Zones); 3. Puglia: from 1071 to 1755 HDD, (C and D Zones); 4. Sicilia: from 707 to 2248 HDD, (from B to E Zones). Table 2 shows for each climatic zone, the number of analysed buildings. It is worth noting that 55% of them are located in the C zone (83 of 151 buildings). Furthermore, for three regions, more than 50% of the investigated buildings are located in the C zone and only in Sicilia, with most of the buildings present in the B zone (54%). Moreover, according to data available from UNI 10349:1994 [36], for each building the global solar irradiance (direct + diffuse) during heating seasons was considered and a climate data-sheet for each region was provided. For each building, a code was appointed together with the

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ACCEPTED MANUSCRIPT climatic zone, the heating season period, HDD, and the global solar irradiance on horizontal surfaces. 2.2 Building Status Most of the public buildings in South Italy fall in climate zone C. A general assessment of this part of Italian building stock reports that most were built before 1970, and just 1/3 of them, are equipped with wall thermal insulation, while common characteristics are: reinforced concrete structure, floor area greater than 500 m2, small window surfaces per room and high electrical consumption due to air conditioning systems [35]. In the following section, a detailed analysis of geometrical and energy characteristics of the investigated building stock is reported. 2.2.1 Building Description In addition to the climatic context definition, data on each building was collected based on the parameters of: use, building typology, opaque and transparent envelope characteristics, floor area, room height, gross heated volume, exposed surfaces, S/V ratio, heating/cooling system, DHW system, monthly thermal and electrical energy consumption, lighting system typology and exploitation of Renewable Energy Sources (RES). In Fig. 2 the building use and its classification according to DPR 412/93 [33] is reported. The following graphs (Fig. 3) represent the percentage of the building use category and a deepening of the data shown in Fig. 2 at the regional level, where:  E.2 are Office buildings;  E.4 are Museums, libraries, congress halls;  E.6 are Gymnasium and Sports buildings;  E.7 are School buildings. As it is possible to see, in all cases most of the buildings belong to the E.7 category, school buildings. In all regions, the highest percentage of buildings is school buildings with a range between 50% and 70%. A percentage between 23% and 35% of the buildings belong to category E.2: office buildings, while there are very few buildings belonging to the categories E.4 and E.6. 2.2.2 Thermophysical features Several construction typologies and correlated thermophysical characteristics [37-45] have been studied. Indeed, energy demand in buildings depends on a combination of several parameters, such as climate [46], envelope typologies [37-40], orientation [41], occupant behaviour, and intended use [42-45]. Indeed, assessing a building’s energy performance requires substantial input data describing constructions, environmental conditions, envelope thermo-physical properties, geometry, control strategies, and several other parameters [14]. Table 3 reports the widespread use in each region. In each building, there are different compositions and thickness of layered envelope, so the Uvalues listed in Tables 4 and 5 should be considered only as indicative. Regarding the distribution of window glass typologies, the double-glazing is the most common in Puglia and in Calabria, while single glazing is still very frequent in Campania and Sicilia. The most widespread frame material is aluminium, with or without thermal break profiles. As in the case of opaque envelope, in each building there are different shapes and dimensions of windows, so the transmittance values listed in Table 3 are also, in this case, indicative. Other materials for 5

ACCEPTED MANUSCRIPT windows, which sometimes occur, are: iron and wood; French-doors do not usually have shading devices (louvers, blinds, overhang, etc.). By the analysis of these data, averaged values of building construction typologies for a “typical public building” in South Italy have been calculated (Tables 4 and 5). These tables collect minimum, maximum, and average values of thickness and typical U-value of the envelope elements (the most frequent). Furthermore, the database includes aggregate information about: shape factor (S/V), window surface/opaque envelope ratio (Sglass/Sopaque), heated gross volume (Vgross), heated floor area (Shf-area) and average U-value of outer wall or roof (Uaverage). Minimum, maximum and average values of geometrical features and U-values, for each region, are shown in Table 6. The values in italic-bold are the minimum values, the bold values are the maximum. The absolute minimum, maximum, and average values in Table 7 are reported. 2.3 HVAC systems, lighting equipment, and plants A detailed energy analysis of each building was carried out. Natural gas and electricity consumption (kWht and kWhe) have been collected from the utilities invoices while end-uses of the energy vectors have been derived from the energy audits. First, an analysis of HVAC systems and other energy consuming equipment was conducted. 2.3.1 HVAC System Almost all the buildings are characterised by the simultaneous presence of different plant typologies for the space heating, cooling and hot water production. This “typical” heterogeneous presence of plant systems hardly allows making a comparative analysis among the various buildings. The following tables (Tables 8 and 9) collect the HVAC systems features per region. Generally, the most diffuse space heating system is composed by a natural gas boiler as generator and regulated through an external climatic probe, a hot water distribution system and radiators as emitting system. Usually this is a heating system with low performance (global efficiency about 70%). The most diffused cooling systems are mono-split and all-air systems with an Air Handling Unit (AHU). Several buildings use both natural gas and a small electrical boiler for Domestic Hot Water (DHW) production. The use of DHW is very limited; indeed, there are many buildings that do not have DHW systems. All data are collected in the Fig. 4 in which it is possible to evaluate the geographic distribution of plant typologies. In particular, the most common in each region are:  Central air to water Heat Pump (38.68%) and Standard Natural Gas Boiler (39.62%) in Sicilia;  Mono-split HP (47.14%) and Standard Natural Gas Boiler (42.87%) in Puglia  Standard Natural Gas Boiler in Campania (48.78%) and Calabria (50.94%). 2.3.2 Lighting System An accurate classification of indoor and outdoor light sources typologies was carried out. In Fig. 5, the distribution of light sources typologies for all regions is reported. The most common typologies are: fluorescent lamps (present in 85-100% of the buildings); incandescent lamps (absent in Calabria and present in 3-38% of the buildings); high pressure sodium (absent in Calabria and present in 3-38% of the buildings ); mercury lamps (in particular outdoor only in Calabria and Campania); metal halide lamps (present in 3-25% of the buildings); 6

ACCEPTED MANUSCRIPT iodide lamps (present in about 90% of the buildings in Puglia, and about 25-60% in the other regions) and LED (absent in Sicilia and little presence in other regions). Generally, the fluorescent lamps are the most common for indoor lighting; instead, external spaces are often lit with iodide lamps [47]. Indeed, it is demonstrated that indoor daylighting conditions can significantly reduce energy consumption due to artificial lighting and can improve indoor visual comfort [48, 49]. As can be seen in Fig. 6, most of the electrical consumption is attributable to lighting installations. In Sicilia, the lighting systems are responsible for about 55% of electricity consumption, while in the rest of the regions such lighting systems have a share of approximately 40%. 2.3.3 Renewable Energy Plants The RES plants are few and only based on solar energy. Looking at Table 10, should be noted that in Puglia and Sicilia no solar thermal system is installed. PV systems are installed in 7 buildings in Sicilia, 2 in Calabria, 11 in Puglia and 9 in Campania. Only in 2 buildings in Campania and in 2 in Calabria are there both PV and thermal solar systems. The following Fig. 7 shows the percentage of buildings with PV systems. In the whole sample, only 11 PV systems are installed; their geographic distribution is highlighted in Table 11. 2.3.4 Electricity end-uses Systems and devices consuming electricity are: appliances (printers, computers, photocopiers and similar devices), lighting system, lifts, boilers for DHW production and HVAC systems. Energy end-uses split by regions have been analysed. Fig. 8 shows the breakdown of electricity demand in several end-uses. Most electricity consumption is due to lighting and appliances. For this reason, one of most effective retrofit actions could be the replacement of lighting sources with energy efficient lamps [50-52]. 2.3.5 Global Energy Performance UNI/TS 11300-1, 2, 3, 4 (2002/91/CE) standards have been used to assess the energy performance of the 151 buildings. For each building, overall primary energy demand (PE) was calculated evaluating the specific yearly primary energy consumption of systems able to maintain a temperature of 20°C in heated spaces. Electricity consumption was converted to primary energy assuming a conversion factor form TEP/kWhelectric to kWhprimary/kWhelectric equal to 11.86 x 103. The PE includes not only the yearly global demand for space heating but also the DHW production, and it is measured in kWh/m3year. Each calculated PE was compared in the following figures with the average regional value. Fig. 9 shows that in Puglia, only few PE values exceed the average regional value; on the contrary, in Sicilia and Calabria there is a greater variance. Generally, it can be seen (Table 12) as the nonresidential buildings in South Italy are characterised by an average PE of about 28.40 kWh/m3year. Observing the differences between the overall average value and the regional average values (Fig.10), it is worth noting that in Campania and Sicilia there are lower PE (from 3.61 to 3.42

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ACCEPTED MANUSCRIPT kWh/m3year lower than the general average), while in Puglia and Calabria the PE are higher (about 4.49 to 2.53 kWh/m3year). The Normalised Energy Index for Heating (IENH) and Normalised Energy Index for Electrical consumption (IENE) have been calculated for each building. Equations (3) and (4) describe their definition [53]:

1000  ATC  Fe  Fh 

I ENH 

V

gross  HDD 

  Wh  m3  HDD  year   

(1)

 AEC  Fh 

 Wh  (2)  m 2  year  Vgross   where: ATC represents the Average Thermal Consumption, AEC represents the Average Electrical Consumption, Fe is the normalisation factor depending on building use and S/V, Fh is the normalisation factor according to plants operating hours, Vgross is the heated gross volume of building and HDD are the Heating Degree Days. In order to compare the data of all regions, the average data of the two indices are reported (Fig.11 and Fig.12). I ENE 

An analysis of electrical and heat energy requirements has provided the characterisation of an average building with the following data:  about 240 MWht of heat demand per year and an IENH index of about 7.2 Wh/m3HDDyear;  about 196 MWhe of electricity demand per year and an IENE index of about 7.5 kWh/m2year. 3. RETROFIT ACTIONS In order to improve the energy performance of each building, several actions have been examined. For each measure, cost, payback time (PbT), and achievable reduced PE have been provided. As an output of the energy audits, 10 possible typologies of retrofit actions have been considered: 1. Upgrade of building management and automation systems; 2. Upgrade of lighting system; 3. Opaque envelope insulation; 4. Upgrade of HVAC system; 5. Installation of PV system; 6. Upgrade of transparent envelope performances; 7. Upgrade of thermoregulation system; 8. Installation of solar thermal system; 9. Upgrade of water pumping system; 10. Other. The evaluation of the best retrofit actions was based on a simultaneous analysis of energy performances and economic feasibility. Energy effectiveness related to their implementation was assessed by comparing the PE figures ex-ante and ex-post (Fig. 13). From the point of view of energy saving, the best influencing retrofit action is the improvement of the opaque envelope (action 3). It can be assumed that retrofit actions that allow an energy saving less than 5% are not suitable from the point of view of energy improvement. In order to better evaluate the impact of the retrofit actions it is useful to determine the energy effectiveness (EEff) as:

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ACCEPTED MANUSCRIPT

 PE

EEff=100×

ex-ante

-PE ex-post 

PE ex-ante

=

ΔPE PE ex-ante

[%]

(3)

Values are represented in Fig. 14. Although the analysis of Figs. 13 and 14 identifies the improvement of the thermal insulation characteristics as the most effective retrofit action, a proper evaluation of priorities cannot underestimate the total cost. In this specific case, the simultaneous assessment of the high cost of the retrofit action and the limited economic convenience, leads to a high PbT. For this reason, the priorities must be evaluated always taking into account energy efficiency and economic feasibility [54]. Looking at economics, the specific cost for each retrofit action and the relative Pbt are collected in Table 13. It is clear that the best performances are obtained with action 1. The average cost of saved kWh of primary energy was evaluated considering the ratio between the total cost and the PE. Results are plotted in Fig. 15 and according to these, the retrofit action 4 for upgrading the HVAC system and retrofit action 9 for upgrading the water pumping system has the highest and lowest average cost of saved kWh, respectively. These results emphasize as the only energy evaluation of a retrofit action is wrong, because the feasibility of a retrofit action is the simultaneous function of the economic and energy aspects. Dividing the average cost of saved kWh by the effectiveness, it is possible to extrapolate a ranking that simultaneously takes into account the two aspects. Fig. 16 shows that the best retrofit action, simultaneously taking into account costs and effectiveness, is the improvement or replacement of HVAC systems (action 4). It is clear that these considerations are based on average values and are related to a sample of buildings. A correct choice and design of retrofit actions should be based on actual data and information of a given building, i.e. as an output of specific energy audits. On the other hand, PA often have a scarcity of resources not only for implementing retrofits but also for conducting building audits. By the way, a preliminary selection of the most effective measures, together with their effectiveness, based on the knowledge of a restricted set of data describing the building, could help the decision-maker toward further analysis. Since detailed thermophysical modelling of a building-plant system is a complex task and the related phenomena are dependent on several factors, it is not possible to define a unique function that allows calculating the PE value for a given case study. In this case, the application of an alternative methodology, such as artificial neural networks, is much more effective. These models are able to build a relationship, based upon the collected data, among the characteristic parameters of the building-plant system and the PE value and/or the convenience of the retrofit action. In the following chapter, after a brief description of the neural network technique, the sample of data relating to the 151 buildings will be used to develop two neural networks aimed at assessing the PE value and the best retrofit option for a generic building not included in that set. 4 ARTIFICIAL NEURAL NETWORK The knowledge of thermal, geometric, economic, and energy data, that characterise a set of nonresidential buildings of South Italy, represents important information, which should be the basis for a decision support tool. PA could use it in order to simplify the choice of the best efficiency action to be implemented in the existing buildings. This aim should be achieved through the implementation of software tool that allows predicting, with good approximation, the energy 9

ACCEPTED MANUSCRIPT quality of a building, by knowing only some selected parameters such as climate data, thermophysics and geometrical characteristics [21, 22, 55, 56] and building HVAC systems. The analysis of the energy balance through a detailed simulation model, that describes the dynamic energy behaviour of a building, with or without plant, can sometimes be difficult to realise. In this way, an alternative approach is the use of evolutionary algorithms, such as artificial neural networks (ANN), which are not based on knowledge of the dynamics of the system, but on the intrinsic relationship between a significant sample of the characteristic variables of the case study and the investigated phenomenon. These models are very useful whenever the phenomenon is characterised by considerable complexity and interdependence of many factors. Based on the data collected by the energy audits it was possible to create and to train two neural networks. 4.1 General Description of ANN An ANN consists of many interconnected processing nodes known as neurons that act as microprocessors. Each artificial neuron (Fig. 17) receives a weighted set of inputs and produces an output. The activation potential (Ai) of an ANN is equal to [57]: Ai    wij x j  b j 

(4)

where N is the number of elements in the input vector xi, wij are the interconnection weights, and bi is the “bias” for the neuron [58]; the bias is a coefficient that controls the activation of the signal handled by the ANN. The neuron output depends only on information that is locally available at the neuron, either stored internally or arrived via the weighted coefficients. The neuron output yi is calculated by the summation of weighted inputs with a bias through an “activate on function” as follows:   Ai       wij x j  b j   (5)

The activation function is intended to limit the output of the neuron, usually between the values [0, 1] or [–1, +1]. Typically, it is used in the same activation function for all neurons in the network, even if it is not necessary [59]. Generally, an artificial neural network consists of multiple interconnected artificial neurons, arranged in several layers. The use of ANNs often makes it possible to identify correlations between data that are very complex to assess. Generally, an ANN is divided into three parts: the input layer that collects the inputs xi, the hidden layer hi, and the output layer that issues the outputs yi. If a neural network is composed of a single layer of unidirectional connections from the input nodes to output nodes it is called the perceptron. This configuration is the simplest and is not able to solve not linearly separable problems. For these kinds of complex problems, it is more useful to use a multilayer perceptron (MLP) ANN that is a feed forward ANN model that maps sets of input data onto a set of appropriate outputs. The feed forward was the first and arguably simplest type of ANN developed. In a feed forward ANN the connections between the units do not form a directed cycle; the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. In this way, there are no cycles or loops in the network. According to the above definitions, a feed forward MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function [60]. Our aim is to discover a function approximation among the collected data and the PE and cost. Function approximation seeks to describe the behaviour of very complicated functions by ensembles of simpler functions [61]. 10

ACCEPTED MANUSCRIPT 4.2 ANN ability to determine the Energy Performance of a building In order to assess the energy performance of a building-plant system a neural network was developed that was able to predict the value of PE in kWh/m3year. Among the different networks tested, the best performances are achieved with the following configurations: • ANN with n. 19 inputs and n.1 output; • MLP with 2 hidden layers of neurons, the first with 15 neurons, the second with 3 neurons; • A hyperbolic tangent sigmoid activation function; • A training period of 1000 epochs. The data used to train the network were obtained by the energy audits; the input data chosen for each individual building were: 1. Heating Degrees Day HDD; 2. Monthly horizontal solar irradiance G; 3. Gross heated Volume Vgross; 4. Shape factor S/V; 5. Useful Surface Suseful; 6. Opaque Surface Sopaque; 7. Glazed Surface Sglass; 8. Average Thermal transmittance of glass surface Uglass; 9. Average Thermal transmittance of opaque surface Uopaque; 10. Global efficiency of the heating plant g; 11. Number of operating hours of the plants in the heating season h; 12. Ratio of glass and opaque surface Sglass/Sopaque; 13. Present or absence of PV plants; 14. Electrical power from photovoltaic plants; 15. IENH index 16. IENE Index 17. Heat energy requirement [kWht]; 18. Electrical energy requirement [kWhe]. Furthermore, to improve the performance of the ANN, a further input was implemented that correlates, in a synthetic way, the energy balance of the building-plant system (K):

K  A B  0.024  HDD  U average     g     5.7  1.57   h  Susefull  B  1000  Vgross   A

S V

(6)

where the values 5.7 [W/m2] and 1.57 [W/m2] are respectively the internal gains linked to the occupants and the electrical equipment. The validation of the ANN results is assured by comparing the results with data not used for the training phase (about 15% of the total data). After having checked the data in order to eliminate outliers, 116 records were used for training and 17 for the validation of the results. The validity of the ANN is demonstrated in Figs. 18 and 19 that represent the Mean Absolute Error (MAE) frequencies for the training and validation phases for the first ANN.

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ACCEPTED MANUSCRIPT In statistics, the MAE is a quantity used to measure how close forecasts or predictions are to the outcomes. In the first case it was calculated an MAE of 1.99 kWh/m3year (Fig. 18); in the training phase an error of 0.125 kWh/m3year (Fig. 19). Furthermore, in Table 14 data related to Median and Standard Deviation (StDev) values are collected. The Median is the value separating the higher half of a data sample, a population, or a probability distribution, from the lower half; it may be thought of as the “middle” value of a data set. The standard deviation (StDev) is a measure used to quantify the amount of variation or dispersion of a set of data values [62]. 4.3 ANN ability to evaluate economic feasibility The second network was implemented in order to correlate the improvement of PE with the economic investment. In this case the network structure that has shown the best performance is:  N. 10 inputs and n. 1 output;  MLP with 2 intermediate layers of neurons, the first with 18 neurons, the second with 13 neurons;  A hyperbolic tangent sigmoid activation function;  A training period equal to 10000 epochs. The input data chosen for each individual building were: 1. Heating Degrees Day HDD; 2. Gross heated Volume Vgross; 3. Shape factor S/V; 4. Global efficiency of the heating plant g; 5. IENH index 6. IENE Index 7. PEex-ante obtained by the first ANN; 8. PEex-post; 9. Difference between the PEex-ante and PEex-post = PE; 10. Typology of retrofit action. The output of the network is the cost of retrofit action normalised per m3 of heated volume as a function of PE. After analysing the data in order to eliminate anomalous records, 527 records were used for training and 88 records were used for validation of the results. For the second ANN it was calculated an absolute average error of 0.012 €/m3 (Fig. 20); in the training phase an error of 1.032 €/m3 (Fig. 21). The following Table 15 collects the statistic parameters that describes the results of the second ANN. These results are better than the previous showed in Table 14, underlining the optimal performance of the ANN.

12

ACCEPTED MANUSCRIPT 5. DECISION SUPPORT TOOL The two developed ANNs are aimed at creating a tool easily implemented into any software. To this purpose, the "function approximation" is coded in a Dynamic Link Library (DLL) compatible with the standard development environment Microsoft .NET (Fig. 22). The use of the tool enables the decision maker to conduct a fast assessment of: 

PE of a generic building;



Specific maximum cost and PbT of a retrofit action.

In this way, even a non-expert user, by knowing some simple information such as climate data, geometric, thermophysical characteristics, and existing HVAC systems, can immediately have a first rough evaluation of PE and an economical indication about the feasibility of a retrofit action. In detail, the software tool provides two tabs. The first provides the collection of the 18 input data defined in the first network, and the second provides the collection of the 10 data input of the second ANN. The user can perform a NN evaluation by clicking on the command button “Calculate”. The neural evaluation process is instantaneous and allows the user to instantly get the PE value in the first tab and the identification of best retrofit action and its PbT in the second tab. 6. CONCLUSION The study starts from the results of research coordinated by DEIM on the behalf of UPI and developed under the project POI Energy. It deals with energy efficiency actions for buildings and plants that are property of provinces in the regions of Puglia, Campania, Calabria, and Sicilia. The individual data, although very useful for the local decision-maker, remains confined to the single building and does not allow generalisation. A correct choice and design of retrofit actions should be based on actual data and information of a given building, i.e. as output of specific energy audits. On the other hand, PA have a scarcity of resources not only for implementing retrofits but also for conducting building audits. In addition, a preliminary selection of the most effective measures, together with their effectiveness, based on a restricted set of data depicting the building could provide guidance to the decision-maker toward further analysis. In this way it is necessary to synthesise data of the entire database and use them in complex algorithms and original methods to identify the similarities and correlations for understanding of the actual non-residential building and to create correct energy planning. The present work has included a first stage of statistics relating to the data of the energy audits of 151 buildings. The analysis was conducted at the regional level, and based on the climatic zones. This study helped to identify some thermophysical and geometric characteristics and typical plant installed in non-residential buildings of South Italy such as U-Value of the enclosure, the thermal system topologies and the used fuel, and thus the average primary energy demand. In this way, typical characteristics and energy performance of a typical non-residential building of South Italy are known. The ranking priorities shown in Fig. 16 imply that the most relevant measures foreseen in any Sustainable Energy Action Plans should be: the upgrade of the HVAC system; the upgrade of transparent envelope performances; the upgrade of building management and automation systems; the upgrade of the lighting system. Furthermore, data of energy audits were used for the training of an artificial neural network for the prediction of the primary energy demand of a generic building. 13

ACCEPTED MANUSCRIPT In this way, the local decision-maker is able to make more informed choices by having the energy information of a generic non-residential building, knowing only a restricted set of characteristics, and without the need to improve a complex simulation model. In conclusion, it is emphasised that the application of this tool can be useful to improving a second ANN that provides information about other characteristics, for example, energy, environment, and economic values of retrofit actions. It is possible to see in Figs. 18-21 that the performances of the two ANNs were generally good. The results are characterised by low absolute error values in the training and validation phases. Both ANNs have been integrated into a decision-making tool that can be easy utilised by stakeholders to select buildings and actions on which to focus their efforts and resources for further investigation and analysis. Acknowledgements This work has been funded by Unione delle Province d'Italia (UPI) in the framework of the project “Realizzazione dell’intervento di diagnosi energetica delle strutture pubbliche provinciali ai fini dell’efficientamento energetico”, POI 2007-2013 “Energie Rinnovabili e Risparmio Energetico”, UPI CUP F76B11000010007. In addition, we would like to thank Eng. Enrico Spera for his collaboration aimed at achieved the goal of this work.

14

ACCEPTED MANUSCRIPT

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ACCEPTED MANUSCRIPT 57. Lo Brano V, Ciulla G, Di Falco M. Artificial neural networks to predict the power output of a PV panel. International Journal of Photoenergy, 2014. 58. Haykin S. Neural Networks: A Comprehensive Foundation, MacMillan, New York, NY, USA, 1994 59. Pacelli V, Azzollini M, An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications 2011, vol. 3, no. 2,pp. 103–112. 60. Asadi E, Silva MG, Antunes CH, Dias L. Multi-objective optimization for building retrofit strategies: a model and an application, Energy and Buildings 2012;41:81–87 61. Principe JC, Euliano NR, Lefebvre WC. Neural and adaptive systems: fundamentals through simulations with CD-ROM, 1999. John Wiley & Sons, Inc.. 62. Fobers, Evans, Hastings and Peacock, Statistical distributions, Fouth edition, Wiley, ISBN 978-0-470-39063-4

18

Fig. 1. Thematic flow-chart

ACCEPTED MANUSCRIPT 80%

The highest percentage are School buildings

Percentage of Buildings

70% 60% 50% 40% 30% 20% 10% 0% School building

Offices Calabria

Sport

Library

Campania

Puglia

Museum Sicilia

Fig. 2. Building percentage use for each region

University

Research laboratory

ACCEPTED MANUSCRIPT Puglia

Sicilia

23,68%

36,54%

2,63%

59,62%

60,53%

13,16%

3,85%

Calabria

Campania

3,33% 23,33%

37,50% 50,00% 70,00%

3,33%

6,25% 6,25%

E.2

E.4

E.6

E.7

Fig. 3. Percentage distribution of buildings sample for each use category

ACCEPTED MANUSCRIPT Calabria

Campania

5,66% 5,66%

0,00% 14,63%

19,51% 37,74%

12,20% 4,88%

50,94% 48,78%

Puglia 2,86%

Sicilia

0,00%

1,89%

7,14%

47,14%

38,68%

39,62% 42,86%

7,55% 12,26%

A.H.U.

Condensing Boiler Heat Pump

Standard Boiler

Mono-split

Fig.4. Regional percentage distribution of HVAC system topologies

ACCEPTED MANUSCRIPT 100%

Calabria

Campania

90%

Percentage of Buildings

80% 70% 60% 50% 40% 30% 20% 10% 0%

Fig. 5. Typology of lighting sources per region

Sicilia

Puglia

ACCEPTED MANUSCRIPT Electrical Consumption Calabria

39%

Sicilia

61%

55%

Campania

45%

39%

Puglia

61%

41% 0%

10%

20%

59% 30%

40% Lighting

50%

60%

70%

80%

Other

Fig. 6. Lighting consumption rate and other electrical consumption

90%

100%

ACCEPTED MANUSCRIPT Puglia

Campania

29%

71%

28%

72%

Sicilia

Calabria

13%

87%

Building with PV system

7%

93%

Building without PV system

Fig. 7. Percentage of PV system installation for each region

ACCEPTED MANUSCRIPT 60% 50% 40% 30% 20% 10% 0% Lighting

Appliances

Calabria

Campania

DHW production, air conditioning and ventilation Puglia

Lifts

Sicilia

Fig. 8. Analysis of electrical consumption per end-use and per region

ACCEPTED MANUSCRIPT Puglia

Sicilia

100

100

80

80

60

60

40

40

20

20

0

0

Campania 100

Calabria 100

80

80

60

60

40

40

20

20

0

0

Primary Energy [kWh/m3year]

Average Primary Energy [kWh/m3year]

Fig. 9. Building Specific Primary Energy demand distribution in each region

ACCEPTED MANUSCRIPT 35 30

[kWh/m3anno]

25 20 15 10 5 0 Puglia

Campania Regional PE

Sicilia

Calabria

Average PE

Fig. 10. Comparison between overall PE and regional

ACCEPTED MANUSCRIPT Heat Energy Requirements [kWht]

[Wh/m3HDDyear]

Regional IenH 300000

10 9 8 7 6 5 4 3 2 1 0

250000 200000 150000 100000 50000 0 Puglia

Campania

Sicilia

Calabria

Puglia

Campania

Fig.11. Regional IENH index and heat energy requirements

Sicilia

Calabria

ACCEPTED MANUSCRIPT

[kWh/m2year]

Regional IenEl

Electrical Energy Requirements [kWhe]

10 9 8 7 6 5 4 3 2 1 0

300000 250000 200000 150000 100000 50000 0 Puglia

Campania

Sicilia

Calabria

Puglia

Campania

Sicilia

Fig. 12. Regional IENE index and electrical energy requirement

Calabria

ACCEPTED MANUSCRIPT 100

40 34

90

35 30

70

25

60 50

20 14

40 30 20

14

14

15

11

9

7

10

6

4

3

10 0

5 0

1

2

3 PE ex-ante

4

5

PE ex-post

6

7

8

9

10

Energy Saving Percentage

Fig. 13. Comparison of PE ex-ante and ex-post retrofit action and energy saving

[%]

[KWh/m3year]

80

ACCEPTED MANUSCRIPT Effectiveness 40 35 30

[%]

25 20 15 10 5 0 1

2

3

4

5

6

7

8

Fig. 14. Energy Effectiveness for each retrofit action

9

10

ACCEPTED MANUSCRIPT

Average cost of saved kWh 70000

[€m3year/kWh]

60000 50000 40000 30000 20000 10000 0 1

2

3

4

5

6

7

8

Fig. 15. Average cost of primary kWh saved for each retrofit action

9

10

3

5

18,96

18,47

15,10

4

6

7

4,03

6,40

2

39,68

35,43

1

15,52

33,42

[€M3AYEAR/KWH]

97,08

ACCEPTED MANUSCRIPT

8

Fig. 16. Specific cost per effectiveness for each retrofit action

9

10

ACCEPTED MANUSCRIPT Inputs

Weights

x1

w1,j Activaction function

x2

w2,j



x3

w3,j

. . .

. . .

xi

wi,j



yi

Activaction

Transfer function

Threshold

Fig. 17. Artificial neural network schema

ACCEPTED MANUSCRIPT

Fig. 18. Absolute error frequency in the validation phase for first ANN

ACCEPTED MANUSCRIPT

Fig. 19. Absolute error frequency in the training phase for first ANN

ACCEPTED MANUSCRIPT

Fig. 20. Absolute error frequency in the validation phase for second ANN

ACCEPTED MANUSCRIPT

Fig. 21. Absolute error frequency in the training phase for second ANN

ACCEPTED MANUSCRIPT

Fig. 22. Screenshot of user interface of the decision support tool

ACCEPTED MANUSCRIPT

Highlights Energy audits of 151 existing public building located in Southern Italy. Creation and implementation of an organized energy audit database. Artificial Neural Network to develop a decision support tool for the assessment of the energy performance. Energy and economic evaluation of the best refurbishment actions.

ACCEPTED MANUSCRIPT Table 1. Distribution of analysed building stock per region

Region Number of Provinces Calabria 5 Campania 5 Puglia 6 Sicilia 9 25

Number of buildings 30 32 38 51 151

ACCEPTED MANUSCRIPT Table 2. Building sample for each region and for each zone Zone/Region B C D E Tot

PUGLIA 0 (0%) 32 (84%) 6 (16%) 0 (0%) 38

CAMPANIA 0 (0%) 25 (78%) 5 (16%) 2 (6%) 32

CALABRIA 9 (30%) 15 (50%) 5 (17%) 1 (3%) 30

SICILIA 28 (55%) 11 (22%) 9 (18%) 3 (6%) 51

Tot 37 83 25 6 151

ACCEPTED MANUSCRIPT Table 3. Building envelope typologies and thermophysical features Region

Calabria

Part of building

Puglia

U-Value [W/m2K] 0.891

Cavity wall with hollow bricks

External wall_2

Stonework

55

2.298

External wall_3

Hollow-core concrete

34

1.047

Roof_1

Hollow-core concrete

26

1.726

Floor_1

Hollow-core concrete

30

1.625

Aluminium frame with single glass

5.65

External wall_1 External wall_2

Concrete

20

1.218

Stonework with air gap

29

1.595

External wall_3

Brick block

38

0.627

Roof_1

Hollow-core concrete

38

1.094

Floor_1

Hollow-core concrete

33

1.178

53.5

2.797 0.659

Window_1 External wall_1

Aluminium frame with double glass Cavity wall

External wall_2

Cavity wall

52

0.799

External wall_3

Brick block

30

1.029

Roof_1

Hollow-core concrete

51.5

1.105

Roof_2

Hollow-core concrete

56

1.027

Floor_1

Hollow-core concrete

35

1.301

Floor_2

Hollow-core concrete

27

1.891

Floor_3

Hollow-core concrete

40

0.672

Window_1 Window_2

Sicilia

Thickness [cm] 40

External wall_1

Window_1

Campania

Typology

Aluminium frame with double glass Aluminium frame with double glass

3.08 2.97

External wall_1

Hollow brick

30

0.889

External wall_2

Brick block

30

0.999

External wall_3

Stonework

40

1.226

Roof_1

Hollow-core concrete

30

1.779

Roof_2

Hollow-core concrete

35

1.35

Floor_1

Hollow-core concrete

30

1.639

Window_1

Aluminium frame with double glass

3.809

Window_2 Window_3

Aluminium frame with double glass Aluminium frame with double glass

4.026 2.929

Window_4

Aluminium frame with single glass

4.06

ACCEPTED MANUSCRIPT Table 4. Layers and U-values of envelope components in non-residential public buildings Element Component

Thickness [cm] U-value [W/m2K]

Min Max Average Min Max Average

Roof Hollowcore concrete 26 56 40 1.03 1.78 1.35

Floor Hollow-core concrete

Cavity wall

27 40 32.5 0.67 1.89 1.38

40 53.5 48.5 0.66 0.89 0.78

External Wall Stonework Brick Block 29 55 42 1.6 2.3 1.95

30 38 32.67 0.63 1.06 0.89

Hollow-core concrete 30 34 32 0.89 1.05 0.97

ACCEPTED MANUSCRIPT

Table 5. U-values of windows in non-residential public buildings Windows Aluminium with single glass U Value [W/m2K]

Min Max Average

5.65

Aluminium with double glass 2.80 4.06 3.38

ACCEPTED MANUSCRIPT

Table 6. Minimum, maximum, and average values of thermophysical and geometric features of buildings for each region Region

Sh-f-area

Vgross

S/V

Sglass/Sopaque

U-value

[m2] 750 22291 6789 434 17293 5388 396 33757 5532 837 13602 5174

[m3] 1658 123751 26917 2158 45529 20116 1228 94503 27195 7650 12185 27050

[m-1] 0.2 1 0.4 0.2 0.9 0.4 0.2 0.8 0.4 0.2 0.6 0.4

[%] 0.4 37 10.2 4 26 11.4 2 63 18 1.4 81 17

[W/(m2K)] 0.9 2.3 1.5 0.7 2.8 1.5 0.6 2.4 1.6 1.4 2.8 2

average

Puglia Campania Sicilia Calabria

Min Max Average Min Max Average Min Max Average Min Max Average

ACCEPTED MANUSCRIPT Table 7. Minimum, maximum and average values of thermophysical and geometric features of buildings for each region for the entire sample General Values Min Max Average

Sh-f-area [m2] 396 33757 5720.7

Vgross [m3] 1228 123751 25319.4

S/V [m-1] 0.2 1 0.4

Sglass/Sopaque [%] 0.4 81 14.2

Uaverage [W/(m2K)] 0.6 2.8 1.6

Table 8. Typologies of heating system per region HEATING SYSTEM Generation system

Distribution System

Region

Central ised Air to Water Heat Pump

Calabria Campania Puglia Sicilia

10% 21% 5% 80%

Region

Externa l climatic probe

Telemonitorin g

Schedules

Indoor Thermos tat

Manua l

Fan-coil

Calabria Campania Puglia Sicilia

53% 50% 84% 8%

7% 36% none none

17% 50% -

13% 3% 43%

73% 61% 89% 82%

30% 75% 53% 41%

All Air System (A.H.U.)

Condensing N. G. Boiler

10% 18% 7% 0% 13% 25% 16% Control system

Standar d N.G. Boiler

Monosplit HP

Water Steel/ Copper pipes

90% 71% 79% 82%

67% 29% 87% 4%

90% 93% 84% 90%

Refri geran t pipes (Mon osplit HP)

Air Ducts

13% 67% 29% 21% 8% 87% 16% 82% Emitting system Air Radi ducts Air ator terminal heaters s 83% 82% 74% 65%

7% 11% 8% -

27% 32% 32% -

MonoSplit HP 67% 57% 87% 82%

ACCEPTED MANUSCRIPT Table 9. Typologies of cooling system per region COOLING SYSTEM Generation System All air System (A.H.U.)

Centralised Air to water Heat Pump

Water (Steel/Copper pipes)

Distribution System Refrigerant Air pipes Ducts (Multi Split)

Refrigerant pipes (Monosplit)

Region

Mono-split

Water Chiller

Calabria

67%

17%

20%

3%

7%

23%

-

70%

Campania Puglia Sicilia

29% 87% 80%

36% 11% 18%

36% 16%

0% 0% 22%

11% 5% 39%

36% 3% 16%

32% -

21% 95% 76%

Control System

Emitting system

Region

External climatic probe

Telemonitoring

Schedules

Manual

Fan-coil

Air ducts terminals

Mono and multi Split

Calabria

10%

13%

none

70%

17%

10%

67%

Campania Puglia Sicilia

4% 3% 6%

0% 3% 31%

none 3% none

29% 89% 80%

36% 8% 4%

29% 3% 37%

57% 87% 80%

ACCEPTED MANUSCRIPT Table 10. Solar Thermal system percentage per region Region Calabria Campania Puglia Sicilia

Solar thermal system Present Absent 14% 14% 0% 0%

86% 86% 100% 100%

ACCEPTED MANUSCRIPT Table 11. PV systems installed N. PV System Puglia

PV peak power [kWp] Sicilia Campania

1 2 3 4 5 6 7 8 9 10

28.35 10.15 10 40 60 60 20 10 48 104

81.9 51.3 27 27.5 17.1 199 3 2.7 49 --

3.5 80 49 20 19.8 19.8 19.8

11

90

Average Peak Power

43.68

-50.94

-30.27

----

Calabria 6 10 ---------8.00

ACCEPTED MANUSCRIPT Table 12. Regional average PE values

Average PE Region kWh/m3year Puglia 32.89 Campania 24.79 Sicilia 24.98 Calabria 30.93 Tot Average 28.40

ACCEPTED MANUSCRIPT Table 13. Cost, specific cost, and PbT for each action

Retrofit action 1 2 3 4 5 6 7 8 9 10

Total Cost

Specific Cost

Pbt

[€] 10,935 67,572 512,702 478,402 60,395 246,051 21,869 18,840 11,473 6,420

[€/m3] 0.48 3.32 22.31 12.53 2.93 10.98 0.99 1.24 1.74 0.29

[year] 6.22 16.14 22.80 44.12 10.11 29.43 6.58 10.20 n.a. 4.40

ACCEPTED MANUSCRIPT Table 14. MAE, Median and StDev data in the 1st ANN 1° ANN Validation Training

Mean -1.9855 -0.1251

Median -2.2934 -1.0774

StDev 8.9417 10.1454

ACCEPTED MANUSCRIPT Table 15. MAE, Median and StDev data in the 2nd ANN 2° ANN Validation Training

Mean -0.0012 1.0322

Median 0.3629 0.5295

StDev 4.6846 5.3556