The emergence of Artificial Neural Networks (ANNs) and their 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), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the 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 linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if 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.

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.