Towards Prediction of Energy Use: A Generalized AI-Based Model for Non-Residential Buildings

The emergence of Artificial Neural Networks (ANN) and its deep learning form, called Artificial Intelligence (AI), opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach in a book entitled "Retrofitting: The Energy and Environment of Buildings" (Gruyter Publishers [5]) and presenting generalized AI modeling in the following paper. This concept utilizes a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and testing) for the optimization of hourly data collected over one full year. The non-residential buildings are less affected by space occupants. This paper examines the feasibility of a uniform, climate-modified technology, as our objective is to create a universal and affordable approach to buildings, assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption in relation to weather conditions was born. This network is designed to forecast electricity consumption for buildings linked to local weather conditions; however, different categories of buildings are grouped in one set. While this will lower the large set precision, our question is whether such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use.