Long-term forecasts of residential energy profiles based on Conv2D and LSTM models (electricity- and gas-based households)

Abstract

For power system operation and expansion of grid-import systems, an accurate forecast model plays an essential role in the better management of household electricity demands. With the aim of finding an accurate forecast model in the proper representation of various household energy profiles, our research objective is centered on the development of a reliable forecast system for a group of 24-household energy consumers. In this energy study, we proposed long-term forecasts of (1) residential energy profiles within the multi-classification framework and (2) energy costing of the household demands using the Keras two-dimensional convolutional neural network (Conv2D) model and long short-term memory (LSTM) models. These high-level Keras neural networks are built to extract multivariate features for household energy consumption modeling and forecasting. The proposed forecast systems utilized a similar model hyperparameter configuration, while the forecast skills are validated with spatial–temporal variation datasets of ten remote locations. The actual costs of household demand and supply are estimated and compared with Conv2D predictions. The finding results (hourly and seasonal predictions and model evaluation) revealed that Conv2D and LSTM forecast systems are promising for household energy forecast solutions. Experimental results of the Conv2D predictive system achieved better forecast skills [correlation coefficient (0.727–0.994) and root mean square error (0.190–0.868)] than LSTM forecasts (0.308–0.987 and 0.278–1.212). However, experimental findings revealed that forecast skills of the predictive systems in residential energy demand predictions are highly influenced by the (1) quality of input datasets, (2) model hyperparameter tuning approach, and (3) learning rate of selected network optimizer(s).