A Forecasting Time Series model Based on Entropy and Fuzzy logic

Electricity Power Consumption Forecasting (EPCF) plays an essential role in global electricity distribution systems that has a significant impact on the operation, control, and planning for the production and distribution of electricity. Due to the complexity, and uncertainty of electricity consumption, especially when the amount of load consumed during different hours is not the same, performing forecasting by using the classical method is inaccurate. To strengthen the efficiency, the time series method that uses a fuzzy approach based on refined entropy is presented in the upcoming article. First, given the specified features, the minimization principle approach of entropy (MPAE) is pursued to define the longitude of each interval in the world of discourse. Secondly, a fuzzy relation matrix of time-invariant is constructed according to the first-order model of fuzzy time series, and the minimum fixed amount of time that the data approach the steady state is obtained using the entropy of the fuzzy set, respectively. Eventually, the forecast results are calculated based on the operation of the maximum combination and the principle of full membership. To show the whole forecasting process, hourly data from July 2022 to September 2022 in Sulaymaniyah / Iraq province is used. Results are compared to the traditional statistical (ARIMA) model, and it indicates that the mean squared error and other criteria of the forecasting error in the entropy based on the fuzzy method are significantly better than the traditional statistical model.