A Hybrid Deep Learning Model with Evolutionary Algorithm for Short-Term Load Forecasting

Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand of electricity with the least amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are many techniques to amicably forecast the demand of electricity. Amongst which the hybrid models show the best result. In this study, a hybrid method integrating Genetic Algorithm (GA), which is an evolutionary algorithm, and long short-term memory (LSTM) network is laid down. For the LSTM network, heuristical trial and error is usually employed to choose the best window size, neuron number, and…