Abstract
Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.
Highlights
Electricity load forecasting has always been a very critical part of electricity system operation
It is known that the load series are nonlinear functions of the exogenous variables [14]. erefore, artificial intelligence models that can handle the nonlinear functions are used for electricity load forecasting
Ree evaluation criterion indexes are used to explore the accuracy of predicted results: the mean absolute (MAE) mentioned in equation (5), the mean percentage error (MPE) mentioned in equation (6), and the MAPE mentioned in equation (7)
Summary
Electricity load forecasting has always been a very critical part of electricity system operation. Many electricity load forecasting models have been proposed. Speaking, these models can be classified into three major categories: time series models, artificial intelligence models, and hybrid models [3]. Ese models are effective in linear prediction problems but cannot handle complex nonlinear load time series perfectly [4]. It is known that the load series are nonlinear functions of the exogenous variables [14]. Erefore, artificial intelligence models that can handle the nonlinear functions are used for electricity load forecasting. Since the load series are nonlinear functions of the exogenous variables, selecting features from influencing factors such as society and weather is an essential step in electricity load forecasting [28, 29].
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