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.
Highlights
Smart meters are being deployed in millions of houses, offering bidirectional communication between the consumers and the utility companies; which has given rise to a pervasive computing environment generating extensive volumes of data with high velocity and veracity attributes
The purpose of this paper is to present models for analyzing, forecasting and visualizing energy time series data to uncover various temporal energy consumption patterns, which directly reflect consumers’ behavior and expected comfort
It is important to note that our proposed approach can be applied to any quantum size for incremental mining, we considered 24 h as the most optimal selection of time-span to retrieve underlying essential information
Summary
Smart meters are being deployed in millions of houses, offering bidirectional communication between the consumers and the utility companies; which has given rise to a pervasive computing environment generating extensive volumes of data with high velocity and veracity attributes. Such data have a time-series notion typically consisting of energy usage measurements of component appliances over a time interval [1]. Utility companies are constantly working towards determining the best ways to reduce cost and improve profitability by introducing programs, such as demand-side management and demand response, that best fit the consumers’ energy consumption profiles. There has been a marginal success in achieving the goals of such programs, sustainable results are yet to be accomplished [2].
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