Short-term load and spinning reserve prediction based on LSTM and ANFIS with PSO algorithm

With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short-term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short-term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short-term predicting of load and spinning reserve is performed using a combination of adaptive network-based fuzzy inference system (ANFIS) and meta-heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS-PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short-term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS-PSO and LSTM is used to reduce the average error and error variance. Here, short-term predicting of load and spinning reserve is performed by adaptive network-based fuzzy inference system (ANFIS) methods and meta-innovative differential evolution, genetic algorithm, and particle swarm optimization (PSO) algorithms. Finally, the ANFIS-PSO combination is selected as the best ANFIS combination with a lower error criterion than other methods. image