FORECASTING OF ELECTRICAL ENERGY CONSUMPTION OF HOUSEHOLDS IN A SMART GRID
نویسندگان
چکیده
This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). proposes new Grey-ANFIS-PSO that is historical data from smart meters in order estimate improve accuracy consumption. will be characterized by coefficients such as Root Mean Square Error (RMSE), Absolute (MAE) Percentage (MAPE). The PSO allow optimally design Neuro-fuzzy forecasting. method implemented Cameroon over 24-years period forecast next years. Using this model, we were able electricity 1867 GWH 2028 0.20158 RMSE 0.62917% MAPE. simulation results obtained show implementation optimized long presents better prediction compared single artificial intelligence models literature Support Vector Machine (SVM) Artificial Neural Network (ANN).Keywords: Forecast PSO, ANFIS consumptionJEL Classifications: C22, C25, C32, C41, C45DOI: https://doi.org/10.32479/ijeep.11761
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ژورنال
عنوان ژورنال: International Journal of Energy Economics and Policy
سال: 2021
ISSN: ['2146-4553']
DOI: https://doi.org/10.32479/ijeep.11761