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Energies 2018, 11(2), 452; https://doi.org/10.3390/en11020452

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Department of Software Engineering at Lakehead Univerity, Thunder Bay, ON, P7B 5E1, Canada
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Received: 24 December 2017 / Revised: 10 February 2018 / Accepted: 12 February 2018 / Published: 20 February 2018
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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Abstract

Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively. View Full-Text
Keywords: big data; energy time series; smart meters; behavioral analytics; energy forecasting; clustering analysis; data mining big data; energy time series; smart meters; behavioral analytics; energy forecasting; clustering analysis; data mining
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Singh, S.; Yassine, A. Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies 2018, 11, 452.

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