A Study on Influence of Ambient Temperature on Load ... - IJIR

Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-3, 2017 (Special Issue) , ISSN: 2454-1362, http://www.onlinejournal.in

Proceedings of 4th International Conference on Current Trends in Engineering, Science Technology and Management

A Study on Influence of Ambient Temperature on Load Forecasting Using R-Tool Shreyas Karnick1, Sunil Kumar A V2, Anandraj3 & Shailesh M L4 1

Assistant Professor, Dept. of E&EE, Acharya Institute of Technology, Bengaluru-560107. Assistant Professor, Dept. of E&EE, Acharya Institute of Technology, Bengaluru-560107. 3 Assistant Professor, Dept. of Mechanical, BVB College of Engineering and Technology, Hubli580031 4 Assistant Professor, Dept. of E&C Acharya Institute of Technology, Bengaluru-560107. 2

Abstract: The demand for electricity has drifted from being static to dynamic in nature over the past few years. This has led to the consequent need to appropriately change the generation capacity and pattern in the country. A power generating company solely depends on the estimate of load required to be supplied in future in order to meet the dynamic load with complete reliability. Load Forecasting hence plays a vital role in operation planning activities in the power system. A number of literature have been published in this regard that employ artificial intelligence techniques and regression models for load forecasting. The load consumption in future depends on the temperature in addition to the past load data. The regression models have mostly considered the past load data of previous day, week, and month at the same time blocks for load forecasting. The influence of temperature on load is very evident in the HVAC systems which finds almost no consideration in the regression models. This paper presents a study on influence of ambient temperature on load forecasting by fitting a Multiple Regression Model in R-Tool, a useful data mining software. Keywords: Load Forecasting, Multiple Regression, Data Mining.

Temperature,

I. INTRODUCTION The unprecedented population growth followed by widespread technological advancements in the recent years have led to increased utilization of electrical energy. The pattern thus involved in energy consumption is highly uncertain due to a number of factors like class of consumers, time of the day, week and month and weather conditions. With uncertain energy consumption or demand, the generating companies face a typical challenge to appropriately vary the generation in accordance with the demand. In order to facilitate the generating utilities to adequately plan and hence to ensure reliable and continuous supply of power to all consumers, load

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forecasting comes across as a vital operational planning activity in the power system. Load forecasting is dependent on a number of factors like time, economic, weather and other random factors [1]. The time of the day, week and month constitute the time factors while economic factors are prevalent when the GDP of a country is considered. Another very important factor that affects load forecasting is the weather conditions. The most common load predictors are the temperature and humidity. A survey published in [2] has shown that out of 22 reports, temperature was used in 13, temperature and humidity used in 3, additional weather parameters used in 3, while only load parameters were used in 3 reports. It is thus evident from the survey that temperature plays a decisive role in accurate load forecasting. The conclusions drawn by D Paravan [3] is that a high positive correlation exists between temperature and load during summer while a negative correlation exists between temperature and load during winter. A number of models have been used for load forecasting. End-use and Econometric approaches have been used for medium and long term load forecasting[4] while similar day approaches, various regression models, time series, neural networks and fuzzy systems have been employed for short term load forecasting. Engle et. al [5] have employed several regression models for the purpose of peak forecasting that incorporate the influence of holidays and time of the day factors for load forecasting. References [6], [7], [8] and [9] have also used a number of regression models applied to load forecasting. This paper mainly concentrates on drawing comparative conclusions on the influence of temperature on load forecasting. The work carried out involves considering historical load data and their respective temperature data as inputs to a multiple regression model. With humongous amount of data collected at every half hour interval, the load data comes across as big data that needs to be appropriately handled using suitable data mining software. In this regard, the multiple regression

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Proceedings of 4th International Conference on Current Trends in Engineering, Science Technology and Management model for load forecasting has been implemented in the R-Tool [10], an effective data mining software.

II. REGRESSION ANALYSIS Regression analysis is a similar day approach model that can be effectively employed for the purpose of electric load forecasting. This type of model involves explicit analysis of the relationship that exists between an dependent variable say “y” and one or more independent variables say “x1,x2,….”. The main objective of regression analysis is to make use of the relationship between the independent and dependent variables to aid the prediction of the dependent variables. The most commonly employed regression model is the linear regression model. The model is employed to determine a linear relationship between the dependent and independent variables. The linear regression model can be broadly classified into simple linear regression and multiple linear regression. The simple linear regression involves a dependent and only one independent variable. Whereas in order to consider the behavior of a dependent variables in terms of more than one independent variable, the multiple linear regression model is used. The relevance of multiple linear regression model for the electricity demand lies in the fact that in the HVAC (Heating, Ventilation, Air-Conditioning and Cooling) environment, the demand for electricity varies with temperature in addition to the time of the day factors. During the summer season, a number of commercial and domestic users use air conditioners which increases the demand for electricity while in the India scenario during winters there is minimal usage of heaters and hence there is less demand for electricity. Hence it’s required to apply the multiple regression model for electrical load forecasting.

III. R-TOOL AND DATA MINING FOR LOAD FORECASTING R-Tool is an open source software that is a language and environment developed by Bell laboratories used extensively for the purpose of data mining. A number of data mining techniques such as clustering, classification, prediction and time series analysis can be realized using the RTool. A number of plots can be conveniently plotted with the aid of the R-software. Also mathematical models and formulae whenever required can be conveniently applied with effectiveness. The R-Tool has a number of advantages that include efficient data handling


capability, array and matrix calculations using a number of simple operations, data analysis tools using a number of intermediate tools, improved graphical facilities and user friendly programming language. Data Mining involves extraction of important knowledge from huge amounts of data in order to aid activities like clustering, classification and prediction processes. With the advent and widespread use of smart meters in the power system, the electrical load data gets recorded every half hour and this reading at every half hour accounts to one block. Hence 48 blocks of data gets recorded for a day. Around 1440 blocks of data gets recorded for a month which accounts to huge amounts of data requiring efficient and effective handling. Data Mining thus aids the purpose of effective load data handling in order to aid a number of planning and operation activities in the power system such as clustering of consumers with similar load consumption pattern and load forecasting.

IV. MULTIPLE LINEAR REGRESSION IN R R-tool has been extensively used for the purpose of data mining. In this regard, work in the paper concentrates mainly on using the R-tool for the purpose of studying the influence of temperature ambient temperature on load forecasting by appropriately employing the multiple regression model for the load data recorded at every half hour along with respective temperature data for the blocks considered for load forecasting. The general mathematical equation for multiple linear regression is given by: y = α + β1x1+β2x2+….βnxn Where, ydependent variable α, β1,β2….βncoefficients x1,x2….xnindependent variables The regression model can be created with a function lm ( ) in R. The value of coefficients are determined in the regression model with the help of input data. With the aid of the calculated coefficients, the dependent variables can be predicted using the independent variables. The lm ( ) function is used to create a relationship between the dependent and independent variables. The syntax for the function lm ( ) is: lm ( y ~ x1+x2+x3……, data) Where, y ~ x1+x2+x3  represents a relationship between independent and dependent variables. data  the vector on which the formula is applied.

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Proceedings of 4th International Conference on Current Trends in Engineering, Science Technology and Management In this paper, the above multiple linear regression model is employed for the purpose of load forecasting. The dependent variable is the blocks of present day load data while the independent variables are the blocks of previous day load data and the temperature of that respective day. With these data, another function called “predict” is employed in order to aid electric load forecasting. This further aids the study on influence of temperature on electric load forecasting.

V. ALGORITHM 1. Step 1: Start 2. Step 2: Read data from .csv file where the data contains blocks of load data and temperature data. 3. Step 3: Normalize all columns of the data in order to provide equal weightage to all factors. 4. Step 4: Fit the multiple linear regression model for the above normalized data using the lm () function. 5. Step 5: Use the predict function on the model that is fit to obtain the forecasted load for the next block of data. 6. Step 6: Convert the forecasted load into matrix using the data.matrix command. 7. Step 7: Plot both the actual load data and the forecasted load data to obtain a comparative study on the influence of temperature on load forecasting. 8. Step 8: Calculate the Mean Absolute Percentage Error (MAPE) using equation: 𝑨𝑷 =

𝟏

∑ =𝟏

𝑨

𝒂

𝒂

𝑨

− 𝒂

𝒂

𝒂

𝒂

Figure 1

Discussion: The results obtained indicate that regression models have significant amount of accuracy and hence less forecasting errors. In the above case it is evident from the graph obtained that the load pattern of forecasted values closely follows the load pattern of actual values. The mean absolute percentage error (MAPE) obtained in this case is 0.0844. Case 2: Load forecasting with temperature variable Result: In this case the temperature variable is considered in addition to previous day load in the multiple regression model and the result thus obtained as shown in Figure-2

9. Step 9: Stop.

VI. RESULTS AND DISCUSSIONS The data used to study the influence of ambient temperature on load forecasting is obtained from a typical area in an industry. The ambient temperature recorded in the area for the respective block of time is also recorded and used for the study. The load forecasting for the successive blocks is carried out by considering data for two days that which is equal to 96 blocks of data. The algorithm explained in section V is converted in a code and executed in R to obtain the multiple regression model for the data considered and load forecast for successive blocks is obtained using the fitted model for two cases: Case 1: Load forecasting without temperature variable Result: In this case the temperature variable is not considered in the multiple linear regression model and the result thus obtained is shown below:


Figure 2

Discussion: In this case, it is evident from the above graph that the load pattern of the forecasted values follows the load pattern of the actual values to an extent only. The mean absolute percentage error (MAPE) in this case when the temperature variable is considered is 0.1213.

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Proceedings of 4th International Conference on Current Trends in Engineering, Science Technology and Management VII. CONCLUSION The regression models show improved performance in electric load forecasting. With multiple linear regression models, more number of factors that influence the load forecast values can be studied in detail. One such factor considered in this paper is the ambient temperature. More over with smart meters, there is humongous amounts of load data being recorded in the utilities that needs to be efficiently handled to ensure usefulness of the data recorded. This efficient data handling can be achieved through data mining techniques and tools. In this view, the paper has employed the R-tool as the data mining software while the multiple linear regression model as the data mining technique to extract useful knowledge of data obtained. The mean absolute percentage error is considered to evaluate the performance of the technique and hence study the influence of ambient temperature on load forecasting. The results thus obtained in the work can be summarized as given in Table-1 Table 1

CASE

Load forecasting without temperature variable Load forecasting with temperature variable

MEAN ABSOLUTE PERCENTAGE ERROR(MAPE) 0.0844

0.1244

It is evident from Table-1 that MAPE for the case when temperature variable is not considered is less than that while temperature variable is considered. It can thus be concluded that the ambient temperature has significant effect on the load forecasting and hence necessary measures to compensate for this effect has to be undertaken in order to ensure accurate forecasting of load that would in turn aid in the generation, transmission and distribution planning of the power system.

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