Big Data Analytics for Dynamic Energy Management in Smart Grids

By Panagiotis D. Diamantoulakis, V. M. Kapinas, George K. Karagiannidis. Big Data Research, 2015

318 citations recorded by the source corpus.

The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of the large volumes of data generated by a vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/39d1927c3caf9185688453f9d8f99af7d6f14126

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In Control and Systems Engineering

Stephen A. Haben

In Electrical and Electronic Engineering

Citations in the corpus

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “At the service provider domain, smart appliances provide information for energy price estimation [23], [103].”
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, background): “1 3 the analytics programs, and identify anomalies and surface unexpected insights from largescale datasets fast (Mahmud et al. 2020; Diamantoulakis et al. 2015).”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “As a result, a large number of companies are now developing BDACs to harness the power of data and radicalize their technological capabilities by introducing data-driven smart energy management [101].”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, background): “In [53], a limited selection of pre-processing methods for short-term, smart grid, load forecasts are reviewed, considering only dimension reduction and clustering.”