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Energies 2017, 10(11), 1905; https://doi.org/10.3390/en10111905

Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics

Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy
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Received: 2 October 2017 / Revised: 31 October 2017 / Accepted: 15 November 2017 / Published: 19 November 2017
(This article belongs to the Section Energy Fundamentals and Conversion)
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Abstract

The present paper is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter. The AC load, at each time step, is separated from smart meter’s aggregate consumption through energy disaggregation methodology. The obtained air-conditioning load and the corresponding historical weather data are then employed as input features for the prediction procedure. In the prediction step, different machine learning algorithms, including Artificial Neural Networks, Support Vector Machines, and Random Forests, are used in order to conduct hour-ahead and day-ahead predictions. The predictions obtained using Random Forests have been demonstrated to be the most accurate ones leading to hour-ahead and day-ahead prediction with R2 scores of 87.3% and 83.2%, respectively. The main advantage of the present methodology is separating the AC consumption from the consumptions of other residential appliances, which can then be predicted employing short-term weather forecasts. The other devices’ consumptions are largely dependent upon the occupant’s behaviour and are thus more difficult to predict. Therefore, the harsh alterations in the consumption of AC equipment, due to variations in the weather conditions, can be predicted with a higher accuracy; which in turn enhances the overall load prediction accuracy. View Full-Text
Keywords: residential buildings; air-conditioning; smart meter analytics; machine learning; short-term load prediction residential buildings; air-conditioning; smart meter analytics; machine learning; short-term load prediction
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Manivannan, M.; Najafi, B.; Rinaldi, F. Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics. Energies 2017, 10, 1905.

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