A novel framework for machine intelligence aided smarter energy management in residential scenario

Abstract

Energy management systems can monitor, optimize, and control energy utilization in residential and commercial applications. Different solutions are available for achieving better energy management. Among the different solutions, load forecasting is considered as one of most preferred technique. It is essential for a load forecasting technique to predict the aggregated loads at the residential level. This paper presents a systematic analysis of different load forecasting models considering a hybrid renewable energy based smart home which uses a solar photovoltaic (PV) system, wind system, and battery storage system for energy storage. In this work, the various forecasting techniques such as time-series models, regression models, ARIMA (Autoregressive Integrated Moving Average) models, and neural network models are analyzed for different load cases. Simulation is performed based on the effect of seasonal bias and condition load shifting on energy generation. The obtained results show that both polynomial and SVM regression model achieves better performance in terms of R2 score and adjusted R2 score of 0.73, and RMSE values of 0.84. It can also be inferred from the analysis that, the random forest regression model and a neural network-based-long short-term memory (LSTM) model exhibit superior results in comparison to other models.