A systematic review of big data in energy analytics using energy computing techniques

Abstract

In big data and machine learning, energy analytics has shown rapid development in the past decade. With this development in energy analytics, the energy data has exponentially increased well in different areas and domains. Energy computing techniques have been used in various domains, particularly in energy data to handle the data analysis process. This paper focuses on explaining the concept and evolution of various research questions formulation, data extraction, quality valuation, search strategy, study selection and reporting the results in energy-oriented domains to evaluate energy-computing techniques. The thermal power plant, power system and smart grid datasets were highly used out of plenty of other datasets discussed and the ANN algorithm is most commonly used to classify the dataset. Different sets of algorithms are used for data analysis, classification, converting structured data to unstructured data, regression, visualization, and forecasting. Journal publications from the year 2012 to 2020 have reviewed and adopted a systematic approach to identify the findings of all relevant research methodologies in the defined research domain.

Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity

“…When the smart grid and big data are combined, it is possible to optimize power generation in power plants, improve customer interaction, improve emergency response to outages in domestic coverage, plan and optimize transmission and smart distribution from the transmission and distribution sides, and efficiently involve DERs and electric vehicles from the commercial side. The following are some of the ways in which big data analytics can be beneficial in the digitalization of MGs [145][146][147][148][149]: When the smart grid and big data are combined, it is possible to optimize power generation in power plants, improve customer interaction, improve emergency response to outages in domestic coverage, plan and optimize transmission and smart distribution from the transmission and distribution sides, and efficiently involve DERs and electric vehicles from the commercial side. The following are some of the ways in which big data analytics can be beneficial in the digitalization of MGs [145][146][147][148][149]:…”

“…The following are some of the ways in which big data analytics can be beneficial in the digitalization of MGs [145][146][147][148][149]: When the smart grid and big data are combined, it is possible to optimize power generation in power plants, improve customer interaction, improve emergency response to outages in domestic coverage, plan and optimize transmission and smart distribution from the transmission and distribution sides, and efficiently involve DERs and electric vehicles from the commercial side. The following are some of the ways in which big data analytics can be beneficial in the digitalization of MGs [145][146][147][148][149]:…”

Performance analysis of demand forecasting in energy consumption based on ensemble model

“…It is one of the five regional load dispatch centers, working under the national load dispatch center. National load despatch centre (NLDC) is claimed, worked and kept up by power system activity corporation of india limited (POSOCO), an auxiliary organization of power grid corporation of India limited [32], [33]. The data  ISSN: 2302-9285 file consists of 2124 instances under the variable names demand met in the evening peak, off-peak, and requirement in evening and off-peak.…”

Quantum Computing in Big Data Analytics: A Comprehensive Review: Assessing the Advancements, Challenges, and Potential Implications of Quantum Approaches in Handling Massive Data Sets

“…For instance, quantum algorithms are being developed for complex data analysis tasks that were previously deemed infeasible. These include optimization problems, pattern recognition, and machine learning tasks, where quantum computing can potentially deliver solutions faster and more efficiently than classical methods (Dhanalakshmi & Ayyanathan, 2021). The systematic review by Dhanalakshmi and Ayyanathan (2021) further underscores the rapid development of big data analytics, propelled by advancements in computing technologies, including quantum computing.…”

“…These include optimization problems, pattern recognition, and machine learning tasks, where quantum computing can potentially deliver solutions faster and more efficiently than classical methods (Dhanalakshmi & Ayyanathan, 2021). The systematic review by Dhanalakshmi and Ayyanathan (2021) further underscores the rapid development of big data analytics, propelled by advancements in computing technologies, including quantum computing. The review highlights the evolution of data analysis techniques, particularly in energy-oriented domains, where the volume and complexity of data have grown exponentially.…”