Energies, Vol. 12, Pages 1891: Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model (Energies)

Load forecasting is of crucial importance for smart grids and the electricity market interms of the meeting the demand for and distribution of electrical energy. This research proposesa hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting geneticalgorithm II (NSGA II) is employed for selecting the input vector, where its fitness function isa multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the outputof the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fedto the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstratean improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models.In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization(ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used foroptimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to testthe model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy.Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063,65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.