Pre-Processing of Energy Demand Disaggregation Based Data Mining Techniques for Household Load Demand Forecasting

Demand side management has a vital role in supporting the demand response in smart grid infrastructure, in the decision-making of energy management, in household applications is significantly affected by the load-forecasting accuracy. This paper introduces an innovative methodology to enhance household demand forecasting based on energy disaggregation for Short Term Load Forecasting. This approach is constructed from Feed-Forward Artificial Neural Network forecaster and a pre-processing stage of energy disaggregation. This disaggregation technique extracts the individual appliances' load demand profile from the aggregated household load demand to increase the training data window for the proposed forecaster. These proposed algorithms include two benchmark disaggregation algorithms; Factorial Hidden Markov Model (FHMM), Combinatorial Optimization in addition to three adopted Deep Neural Network; long short- term memory (LSTM), Denoising Autoencoder, and a network which regress start time, end time, and average power. The proposed load forecasting approach outperformed the currently available state-of-the-art techniques; namely root mean square error (RMSE), normalized root mean square error (NRMSE), and mean absolute error (MAE).