Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings

Today's energy resources are closer to consumers due to sustainable energy and advanced technology. To that end, ensuring a precise prediction of energy consumption at the buildings' level is vital and significant to manage the consumed energy efficiently using a robust predictive model. Growing concern about reducing the energy consumption of buildings makes it necessary to predict the future energy consumption precisely using an optimizable predictive model. Most of the previously proposed methods for energy consumption prediction are conventional prediction methods that are normally designed based on the developer's knowledge about the hyper-parameters. However, the time lag inputs and the network's hyper-parameters of learning methods need to be adjusted to have a more accurate prediction. This paper proposes a novel hybrid prediction approach based on the evolutionary deep learning (DL) method that is combining genetic algorithm with long short-term memory and optimizing its objective function with time window lags and the network's hidden neurons. The performance of the presented optimization predictive model is investigated using public building datasets of residential and commercial buildings for very short-term prediction, and the results indicate that the evolutionary DL models have better performance than conventional and regular prediction models.