Prediction and Electricity Forecasting on the Individual Household Level based on PSO-LSSVM Approach

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PSO-LSSVM. The proposed method outperforms both GRU and LSSVM, two well-established competitors.