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Received: 15 March 2019 Accepted: 8 May 2019 Office and industrial buildings are characterized by very regular occupation patterns and even building systems are normally scheduled (unless they are controlled by energy management systems). So, under these conditions, either at a detail scale (single office) or at a global scale, variations in energy usage (for both HVAC and lighting) may have a strong relation with outdoor conditions. Modelling and forecasting energy use in such large buildings may be essential to prevent energy shortage and black-outs, as well as to take action in terms of adaptive measures to ensure occupants’ comfort conditions. As the number of smart devices to monitor outdoor weather and air quality conditions is constantly increasing, it might be useful to investigate whether parameters derived from such monitoring stations might be used as proxy variables to predict indoor conditions and, above all, energy consumptions. In order to create a dataset to test forecasting models, different office and industrial buildings have been simulated under dynamic conditions by means of the Energy Plus tool as a function of different climatic data. Then, machine learning algorithms (mostly based on artificial neural networks) were used to predict both energy consumptions and indoor environment conditions as a function outdoor parameters. A study of the short term and long term reliability of prediction models is finally presented.

A Machine Learning Approach to Predict Energy Consumptions in Office and Industrial Buildings as a Function of Weather Data