کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6682812 501851 2016 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns
ترجمه فارسی عنوان
تجزیه و تحلیل سری های زمانی با استفاده از سیستم کشویی سیستم بهینه سازی مبتنی بر سیستم کشویی برای شناسایی الگوهای مصرف انرژی ساختمان
کلمات کلیدی
اطلاعات شبکه هوشمند، مدیریت انرژی ساختمان، مصرف انرژی، پیش بینی الگو، تکنیک سری زمان، بهینه سازی متافیزیکی، فراگیری ماشین،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
چکیده انگلیسی
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 0.026 kW h. 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Energy - Volume 177, 1 September 2016, Pages 751-770
نویسندگان
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