Electricity load forecasting in a smart grid system

Smart grid has recently become a common system in many countries, and smart meter is one of the essential components of this system. The consumption data gathered from smart meters allow electricity companies to better understand electricity usage in the future and monitor electricity supply more efficiently. An electricity consumption forecasting model established on the framework of support vector regression (SVR) was proposed in this study. Given that various factors affected consumption patterns, apart from historical data, weather variations and features of a particular time were also considered in this study. Based on this information, the stepwise regression analysis was applied for feature selection. However, the accuracy of the SVR model is largely dependent on the selection of the model parameters. The particle swarm optimization (PSO) algorithm was proposed to determine the optimal values of parameter that improves the accuracy and efficiency of the SVR model. The experimental results show that this method can provide electricity forecasts with 0.70% and 2.55% of mean absolute percentage error (MAPE) for 1 and 24 hours ahead, respectively. This result indicates promising performance in forecasting accuracy. This model is also computationally efficient and can be applied to make predictions within 1 second.