Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models

  •  Abdoulaye Camara    
  •  Wang Feixing    
  •  Liu Xiuqin    


In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors  such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional. 

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