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

Manoj Manivannan, Behzad Najafi and Fabio Rinaldi
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Manoj Manivannan: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy
Behzad Najafi: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy
Fabio Rinaldi: Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, Milano 20156, Italy

Energies, 2017, vol. 10, issue 11, 1-17

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 R 2 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.

Keywords: residential buildings; air-conditioning; smart meter analytics; machine learning; short-term load prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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