Fuzzy-based weighting long short-term memory network for demand forecasting.

Imani, Maryam

One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy expert systems and deep learning methods. In the first step, dependency of previous time instances to the next instance to be load forecasted is achieved through a fuzzy system with 125 rules. Then, the obtained weights are used beside the actual load values as the input of a long short-term memory network for load forecasting. The obtained results on two popular datasets show the superior performance of the proposed method in terms of various evaluation measures.

DEMAND forecasting; FUZZY expert systems; ELECTRICAL load; DEEP learning; FUZZY systems; TIME series analysis

Journal of Supercomputing, 2023, Vol 79, Issue 1, p435

0920-8542

Academic Journal

10.1007/s11227-022-04659-1