A New Entropy-based Feature Selection Method for Load Forecasting in Smart Homes
Omaji Samuel Nadeem Javaid Asma Rafique
AbstractThis paper addresses the challenges of load forecasting that occur due to the complex nature of load in different predicting horizons and as well as the total consumption within these horizons. It is not often easy to accurately fit the several complex factors that are faced with demand for electricity into the predicting models. More so, due to the dynamic nature of these complex factors (i.e., temperature, humidity and other factors that influence consumption), it is difficult to derive an accurate demand forecast based on these parameters. As a consequence, a model that uses hourly electricity loads and temperature data to forecast the next hourly loads is proposed. The model is based on modified entropy mutual information based feature selection to remove irrelevancy and redundancy from the dataset. Conditional restricted Boltzmann machine (CRBM) is investigated to perform load forecasting; accuracy and convergence are improved to reduce the CRBM’s forecast error via a Jaya based meta-heuristic optimization algorithm. The proposed model is implemented on the publicly available dataset of GEFCom2012 of the US utility. Comparative analysis is carried out on an existing accurate, fast converging shortterm load forecasting (AFC-STLF) model since it has a similar architecture to the proposed model. Simulation results confirm that the proposed model improves the accuracy up to 56.32% as compared to 43.67% of AFC-STLF. Besides, the proposed model reduces the average execution time up to 53.87% as compared to 46.12% of AFC-STLF.