Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting

This paper proposes a novel combined electricity demand forecasting method, which based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA).The combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data.Experimental case study shows that the proposed combined method performed better than the other three individual methods and had a higher accuracy. And the proposed method also performed better than the method ESPLSSVM that I proposed before. Electricity demand forecasting, as a vital tool in the electricity market, plays a critical role in power utilities, which can not only reduce production costs but also save energy resources, thus making the forecasting techniques become an indispensable part of the energy system. A novel combined forecasting method based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA) are presented in this paper. Firstly, the combined method uses all the three methods (BP, ANFIS, diff-SARIMA) to forecast respectively, and the three forecasting results were obtained. By multiplying optimal weight coefficients of the three forecasting results respectively and then adding them up, in the end the final forecasting results can be obtained. Among the three individual methods, BP and ANFIS had the ability to deal with the nonlinearity data, and diff-SARIMA had the ability to deal with the linearity and seasonality data. So the combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data. In order to optimize weight coefficients, Differential Evolution (DE) optimization algorithm is brought into the combined method. To prove the superiority and accuracy, the capability of the combined method is verified by comparing it with the three individual methods. The forecasting results of the combined method proved to be better than all the three individual methods and the combined method was able to reduce errors and improve the accuracy between the actual values and forecasted values effectively. Using the half-hour electricity power data of the State of New South Wales in Australia, relevant experimental case studies showed that the proposed combined method performed better than the other three individual methods and had a higher accuracy.

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