Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

doi:10.3390/en8077407

Cited by 62 publications

(20 citation statements)

References 35 publications

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“…These demand response programs can be implemented through smart meters to control appliances so as to change customers' energy consumption patterns. (6) Load modeling and forecasting: accurate load modeling and forecasting is crucial for system operations and resource planning [46]. Using data from smart meters, the daily energy consumption pattern of each customer can be identified.…”

Section: Smart Metering Technologymentioning

confidence: 99%

Smart Distribution Systems

Jiang

Liu

2016

Energies

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Abstract:The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

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Section: Smart Metering Technologymentioning

confidence: 99%

Smart Distribution Systems

Jiang

Liu

2016

Energies

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“…Various analysis methodologies have been developed: they have used the regression model [37][38][39][40]; time-series analysis [41][42][43][44][45][46][47][48][49]; and clustering techniques [46][47][48][49][50][51][52][53][54]. However, most analyses have been aimed at short-and medium-term demand forecasting; relatively few analyses have been directed at tailor-made feedback.…”

Section: Analysis Methodology Of Residential Electricity-use Datamentioning

confidence: 99%

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

2017

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Abstract:Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption-as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

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“…Context-aware association mining through frequent pattern recognition is studied in [14] where the aim is to discover consumption and operation patterns and effectively regulate power consumption to save energy. The work in [15] proposes a methodology to disclose usage pattern using hierarchical and c-means clustering, multidimensional scaling, grade data analysis and sequential association rule mining; while considering appliances' ON and OFF event. However, the study does not consider the duration of appliance usage or the expected variations in the sequence of appliance usage, which is directly related to energy consumption behavior characterization.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

Cited by 62 publications

References 35 publications

Smart Distribution Systems

Smart Distribution Systems

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

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

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