Short Term Electricity Forecasting Using Individual Smart Meter Data

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

doi:10.1016/j.procs.2014.08.140

Cited by 93 publications

(41 citation statements)

References 17 publications

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“…Our choice of models for comparison is based on the wide use of these classification techniques, SVM and MLP, and wide acceptance as two well-known approaches for classification and prediction by the research community [53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Evaluation and Resultsmentioning

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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Section: Evaluation and Resultsmentioning

confidence: 99%

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

2018

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“…The study significantly contributes to integrate cluster analysis with business intelligence to further alleviate the performance. Nearly similar type of study was also carried out by Gajowniczek and Zabkowski [26] where the authors has used machine learning technique to perform forecasting of smart metered data. The study outcome was evaluated with respect to mean squared error and accuracy on the hourlybased data.…”

Section: A Techniques Towards Performance Enhancementmentioning

confidence: 99%

Research Advancements Towards in Existing Smart Metering over Smart Grid

A¹,

Khan²,

Nagaraj³

2017

ijacsa

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Abstract-The advent of smart meters has automated the entire process of billing generation system over commercial energy usage which was previously done using digital meter. Although western countries practice its usage more, it is still unknown to many developing countries along with its power distribution. Hence, this paper reviews the working principle of smart meters along with the brief of basic operation description. It thoroughly investigates the implementation work towards algorithm design and techniques developed that are being carried out in last five years towards smart meters. The paper examines the various significant technology that has evolved to address the problems in smart meter e.g. performance improvement, energy efficiency, security factor, etc. Finally, a set of research gap is explored after scrutinizing the advantages and limitations of existing techniques followed by brief highlights of the feasible line of research to compensate the unaddressed problems associated with research work direction towards smart meters.

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“…Based on NIALM, there have been research attempts devoted to load prediction on the individual household level [16][17][18][19]. They utilize smart meter data enriched with a set of household behavioral data (patterns of home appliances usage) and dwelling characteristics to benefit significant improvement in terms of the accuracy of the forecasts generated at the household level.…”

Section: Literature Review On Related Workmentioning

confidence: 99%

“…The original dataset contained the electricity usage readings of the smart meter at every second, every minute and every hour. From these readings, we extracted the hour loads (in kWh) for the purpose of short-term load forecasting [19,20], and reference information. In this paper, only on-off states related with the above mentioned appliances will be used.…”

Section: Smart Meter Datamentioning

confidence: 99%

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

Gajowniczek

Ząbkowski

2015

Energies

Self Cite

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Abstract:The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home appliances and the time of their usage. In particular, we present and apply a set of unsupervised machine learning techniques to reveal specific usage patterns observed at an individual household. The work delivers the solutions applicable in smart metering systems that might: (1) contribute to higher energy awareness; (2) support accurate usage forecasting; and (3) provide the input for demand response systems in homes with timely energy saving recommendations for users. The results provided in this paper show that determining household characteristics from smart meter data is feasible and allows for quickly grasping general trends in data.

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Short Term Electricity Forecasting Using Individual Smart Meter Data

Cited by 93 publications

References 17 publications

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

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

Research Advancements Towards in Existing Smart Metering over Smart Grid

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

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