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Modeling of Short-Term Electricity Demand and Comparison of Machine Learning Approaches for Load Forecasting

Electricity is a special commodity that has to be kept available at all times. In fact, power plants need to have accurate forecast of electricity demand in order to provide enough electricity for customers. Final customers are able to establish their own power plants to decrease their dependency on the grid. For example rooftop photovoltaic panels are getting more popular among residential customers. It seems that meteorological variables such as solar irradiance play an important role in load forecasting. Moreover, temperature is also a main determinant of electricity demand. In this paper, we propose a model for shortterm load forecasting which consists of hourly weather data (including seasonal variation as well) and historical load data. Machine learning algorithms such as support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) regression and a multilayer neural network (NN) are used for short-term load forecasting. In order to improve the forecast accuracy (smaller mean absolute error) of NN, we propose a dual phase forecasting method. In the first phase, data driven double exponential smoothing (DDDES) is used to generate electricity load forecasts. In the second phase, the results of first phase forecasting are fed into a multilayer NN to have more accurate forecasts of electricity demand. It is shown that NN outperforms the other two methods. Our data analysis shows a significant improvement in terms of performance where maximum mean absolute error (MAE) decreases from 367.26 to 115.30.