ARIMAX Model for Short-Term Electrical Load Forecasting
Shilpa G N1, G S Sheshadri2
1Shilpa G N, Assistant Professor, Department of Electrical and Electronics Engineering, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India.
2Dr. G S Sheshadri, Professor, Department of Electrical and Electronics Engineering, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on November 30, 2019. | PP: 2786-2790 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7950118419/2019©BEIESP | DOI: 10.35940/ijrte.D7950.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The scope for ARIMAX approach to forecast short term load has gained a lot of significant importance. In this paper, ARIMAX model which is an extension of ARIMA model with exogenous variables is used for STLF on a time series data of Karnataka State Demand pattern. The forecasting accuracy of ARIMA model is enhanced by taking into consideration hour of the day and day of the week as exogenous variables for ARIMAX model. Forecasting performance is thus improved by considering these significant load dependent factors. The forecasted results indicate that the proposed model is more accurate according to mean absolute percentage error (MAPE) obtained during the testing period of the model. As the historical load data are available on the databases of the utility, researches in the areas of time series modelling are ongoing for electrical load forecasting. In the proposed paper real time demand data available on Karnataka Power Transmission Corporation Ltd. (KPTCL) website is taken to develop and test the proposedload forecasting model.The power utility system operational costs and its securitydepend on the load forecasting for next few hours. Regional load forecasting helps in the accurate management performance of generation of power plant. Today’s deregulated markets have great demand for prediction of electrical loads, required for generating companies. There has been tremendous growth in electric power demand and hence it is very much essentialfor the utility sectors to have theirdemand information in advance.
Keywords: Artificial Neural Networks (ANN), Autocorrelation, Autoregressive Integrated Moving Average With Exogenous Variables (ARIMAX),Mean Absolute Percentage Error, Partial Autocorrelation, Short Term Load Forecasting (STLF).
Scope of the Article: Forest Genomics and Informatics.