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Special Issue Editor

Dr. Huilian Liao

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Guest Editor

Power, Electrical and Control Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: smart grids; power system analysis; intelligent data analytics

Special Issue Information

Dear Colleagues,

Energy systems around the world are going through a tremendous transformation, driven by technological changes, carbon footprint, policy imperatives and energy efficiency. They become more and more complex with the widely distributed low-carbon technologies (e.g., EVs, energy storage, PV, wind turbines, heat pumps, etc.). To optimally manage this large number of energy resources, smart and flexible energy systems are essential.

Big data and data analytics play important and unreplaceable roles in achieving smart systems that can deliver significant economic and environmental benefits. At present, data are growing at an exponential rate in the power/energy sector (ranging from power grid levels to household levels), thanks to the rapid development of digital technologies. To generate value from this large amount of raw data, proper big data analytics are needed to process/analyze the data and extract useful information that can be integrated and used in the energy systems. With this urgent need, the development of big data analytics for smart system management/operation has attracted great interest in both academia and industry.

This Special Issue aims to present the macro-environment, cutting-edge technologies, methodologies and applications of big data analytics for smart energy systems. Topics of interest for publication include, but are not limited to:

The development of big data analytics in smart power/energy systems;
Applications of big data analytics in the power/energy system context;
Data sources and their standardization for smart power/energy systems;
Regulation, drives, barriers and gaps of big data use in power/energy systems;
Digitalization and communication in smart energy systems to enable big data use;
Synergizing big data analytics in existing EMS/SCADA systems.

Dr. Huilian Liao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart energy systems
big data analytics
smart grids
low-carbon technology
digitalization
communication technologies in smart grids
machine learning
clustering, classification, characterization
data mining
feature extraction

Published Papers (2 papers)

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Research

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21 pages, 13862 KiB

Open AccessArticle

Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant

by Nonthawat Khortsriwong, Promphak Boonraksa, Terapong Boonraksa, Thipwan Fangsuwannarak, Asada Boonsrirat, Watcharakorn Pinthurat and Boonruang Marungsri

Energies 2023, 16(5), 2119; https://doi.org/10.3390/en16052119 - 22 Feb 2023

Cited by 8 | Viewed by 2396

Abstract

Recently, deep learning techniques have become popular and are widely employed in several research areas, such as optimization, pattern recognition, object identification, and forecasting, due to the advanced development of computer programming technologies. A significant number of renewable energy sources (RESs) as environmentally friendly sources, especially solar photovoltaic (PV) sources, have been integrated into modern power systems. However, the PV source is highly fluctuating and difficult to predict accurately for short-term PV output power generation, leading to ineffective system planning and affecting energy security. Compared to conventional predictive approaches, such as linear regression, predictive-based deep learning methods are promising in predicting short-term PV power generation with high accuracy. This paper investigates the performance of several well-known deep learning techniques to forecast short-term PV power generation in the real-site floating PV power plant of 1.5 MWp capacity at Suranaree University of Technology Hospital, Thailand. The considered deep learning techniques include single models (RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU) and hybrid models (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Five-minute resolution data from the real floating PV power plant is used to train and test the deep learning models. Accuracy indices of MAE, MAPE, and RMSE are applied to quantify errors between actual and forecasted values obtained from the different deep learning techniques. The obtained results show that, with the same training dataset, the performance of the deep learning models differs when testing under different weather conditions and time horizons. The CNN-BiGRU model offers the best performance for one-day PV forecasting, while the BiLSTM model is the most preferable for one-week PV forecasting. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Power/Energy Systems)

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Review

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19 pages, 1994 KiB

Open AccessReview

Review of Big Data Analytics for Smart Electrical Energy Systems

by Huilian Liao, Elizabeth Michalenko and Sarat Chandra Vegunta

Energies 2023, 16(8), 3581; https://doi.org/10.3390/en16083581 - 20 Apr 2023

Cited by 2 | Viewed by 3112

Abstract

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning and operation. This paper also discusses the Big Data Analytics (BDA) that can be used to process/analyze the data and extract useful information that can be integrated and used in energy systems. More specifically, this paper discusses typical applications of BDA in energy systems, including how BDA can be used to resolve the critical issues faced by the current and future energy network operations and how BDA contributes to the development of smarter and more flexible energy systems. Combining data characterization and analysis methods, this review paper presents BDA as a powerful tool for making electrical energy systems smarter, more responsive, and more resilient to changes in operations. Full article

(This article belongs to the Special Issue Big Data Analytics for Smart Power/Energy Systems)

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