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

Manoj Manivannan,Fabio Rinaldi,Behzad Najafi

doi:10.3390/en10111905

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.

Highlights

A significant portion of the global energy consumption is due to the consumption of buildings and the corresponding share of the building sector in the total energy consumption in India, Europe and USA is around 40% [1,2]
Combinatorial Optimisation (CO) and Factorial Hidden Markov Model (FHMM) algorithms, which are conventional disaggregation methods implemented in the open source toolkit Non-Intrusive Load Monitoring Toolkit (NILMTK) [24], have been utilized to conduct the disaggregation of the considered building’s aggregate load
The latter lag is due to the delay in the conversion of the radiation heat transfer into a convective one, as the incident solar irradiation will first heat up the walls and the objects inside the building and they will in turn warm up the internal air through convection

Summary

Introduction

A significant portion of the global energy consumption is due to the consumption of buildings and the corresponding share of the building sector in the total energy consumption in India, Europe and USA is around 40% [1,2]. Previous studies have demonstrated that around half of the energy demand of buildings can be attributed to their heating, ventilation and air-conditioning (HVAC) systems [3]. The consumption of air-conditioning systems has a significant impact on the electrical grid and the precise prediction of its variations can provide the grid management with notable benefits such as competitiveness in the day-ahead market, dispatch management, demand-side management and control optimization. HVAC consumption, is developing physical models employing their geometrical and construction characteristics [4], infiltration properties, required ventilation rate, occupancy profiles and other details. One potential solution to deal with the mentioned issue is the development of data-driven models, which correlate the consumed power of air conditioners in the individual buildings with the Energies 2017, 10, 1905; doi:10.3390/en10111905 www.mdpi.com/journal/energies

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