Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

Jui-Sheng Chou and Ngoc-Tri Ngo

Applied Energy, 2016, vol. 177, issue C, 770 pages

Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026kWh. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times. In particular, the system can potentially be scaled up for using big data framework to predict building energy consumption.

Keywords: Smart grid data; Building energy management; Energy consumption; Pattern prediction; Time-series technique; Metaheuristic optimization; Machine learning (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (32)

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DOI: 10.1016/j.apenergy.2016.05.074

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