Application of Big Data in Smart Grids: Energy Analytics

Marlen, Azamat; Maxim, Askar; Ukaegbu, Ikechi Augustine; Nunna, H. S. V. S. Kumar

doi:10.23919/icact.2019.8701973

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…In [158] and [159], K-means is employed to identify neighbors with similar energy consumption patterns. Reference [160] proposes a method that uses the K-means algorithm and smart meter data to establish household load profiles for energy analytics. K-means is also applied to smart meter data from a university campus to establish representative monthly load profile patterns [161].…”

Section: ) Smart Metersmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: ) Smart Metersmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…The enabled understanding of energy consumption profiles empowers users not only to better recognize how much energy they consume but also to compare their consumption profiles to the profiles of other dwellings [139]. This gives households greater control of their energy consumption and enables the adoption of more energy-efficient, and responsible behaviors [139][140][141][142]. Instead of considering the load profiles of buildings individually, it is often sufficient to know the category that better describes the dwelling.…”

Section: Load Profilingmentioning

confidence: 99%

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

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“…The most immediate constraint is the overhaul of the conventional infrastructure that would require a high cost [37]. Apart from this, the smart grid and big data analytics have other challenges to their application, owing to complex systems [38]. Smart grids use various smart components that work together to form a system.…”

Section: Energy Efficiency Monitoring and Managementmentioning

confidence: 99%

Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

et al. 2021

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The power consumption model can be represented in multiple dimensions, and it is proliferating to include structured and unstructured data. Dealing with such heterogeneous data and analyzing it in real-time is an ongoing challenge in the energy sector. Moreover, converting these data into useful information remains an open research area. This study focuses on modeling realistic and efficient power consumption data management in the heterogeneous environment for the Iraq energy sector and suggested a novel hybrid load forecasting model. The proposed system is named the Power Consumption Information and Analytics System (PIAS), which can perform various roles such as data acquisition from mechanical and smart meters, data federation, data management, data visualization, data analysis, and load forecasting. The proposed system has a four-tier framework (Data, Analytics, Application, and Presentation). Each layer is discussed in detail in this study to overcome the anticipated challenges. Furthermore, this study discusses the proposed system by applying two case studies. The first case study discusses power consumption data management, while the second introduces a novel hybrid load forecasting model using Fuzzy C-Means clustering, Auto Regressive Integrated Moving Average (ARIMA), and Gradient Boosted Tree Learner. The dataset used in this forecasting is based on a 1-year duration dated 1st January 2019 to 31st December 2019, on an hourly basis (365 * 24) for the Baghdad governorate. The results showed high accuracy in load forecasting with improved error rates (MAPE, MAE, and RMSE) achievements in comparison with other evaluated models such as standalone ARIMA and Gradient Boosted Trees methods.

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Application of Big Data in Smart Grids: Energy Analytics

Cited by 9 publications

References 17 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model

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