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