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Special Issue "Data Analytics in Energy Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (30 December 2020).

Special Issue Editors

Dr. Ali Ahmadian
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Guest Editor
Department of Electrical Engineering, University of Bonab, Bonab, Iran; Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Special Issues and Collections in MDPI journals
Dr. Mohamed Bin Shams
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Guest Editor
Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain
Interests: big data analytics for fault detection and diagnosis for large scale systems; time series analysis of energy systems; multivariate statistics; data-driven modeling and optimization

Special Issue Information

Dear Colleagues,

The incorporation of big data in the energy industry is receiving renewed interest due to the rise of pervasive computing devices (i.e., sensors that collect and transmit data) and new algorithmic developments in data analysis, data storage capabilities, and machine learning. The integration of data analytics in energy systems can be used to improve the control, monitoring, and efficiency of the industry. These improvements can be applied in the design phase where the nominal operation is defined. Data-based models can also supplement mechanistic models to estimate operating parameters. In the production stage, scheduling (through optimization) can be used as another source of process improvement. However, despite the setting of operational parameters during the design stage, during the actual production and processing stages, undesired disturbances or unforeseen behaviors often take place. Examples of these undesired disturbances are the variations in energy sources, malfunctioning of process instruments, and varying processing times.

Data analytics has drawn a great deal of attention in today’s energy system studies. A large number of viewpoints in ranging from economic studies (cost-benefit analysis, optimal operation, scheduling, etc.) to technical studies (renewable energy modeling, electric vehicles modeling, energy hubs, etc.) involve big data problems. The big data in energy systems have brought several opportunities and challenges simultaneously for researchers. The main challenges in big data analytics and mining include data inconsistency and incompleteness, scalability, timeliness, data reduction and integration, and data security. To deal with these challenges, the big data should be transformed into a reasonable structure using data mining algorithms. The characteristics of big data should be considered in the transformation algorithms that includes “volume”, “velocity”, “variety” and “value”.

This Special Issue is intended to present original research papers with high quality and novelty on “Data Analytics in Energy Systems”.

Topics of interest include, but are not limited to:

  • Data classification
  • Data Clustering
  • Distributed data mining
  • Machine learning
  • Internet of thing
  • Data cleaning
  • Data reduction
  • Data integration
  • Data transformation
  • Cloud data
  • Data forecasting
  • Data management
  • Data visualization
  • Data statistical analysis
  • Data collection
  • Fault detection and diagnosis for energy systems.

Prof. Ali Elkamel
Dr. Ali Ahmadian
Dr. Mohamed Bin Shams
Guest Editors

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 papers will be 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 2000 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

  • big data analytics
  • parallel and distributed computing
  • data mining
  • cloud computing
  • machine learning

Published Papers (12 papers)

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Research

Article
Method for Clustering Daily Load Curve Based on SVD-KICIC
Energies 2020, 13(17), 4476; https://0-doi-org.brum.beds.ac.uk/10.3390/en13174476 - 31 Aug 2020
Viewed by 543
Abstract
Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some [...] Read more.
Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study
Energies 2020, 13(15), 3996; https://0-doi-org.brum.beds.ac.uk/10.3390/en13153996 - 03 Aug 2020
Cited by 3 | Viewed by 1052
Abstract
Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We [...] Read more.
Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
A Machine Learning Pipeline for Demand Response Capacity Scheduling
Energies 2020, 13(7), 1848; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071848 - 10 Apr 2020
Cited by 3 | Viewed by 1212
Abstract
Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR [...] Read more.
Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine
Energies 2019, 12(22), 4287; https://0-doi-org.brum.beds.ac.uk/10.3390/en12224287 - 11 Nov 2019
Cited by 6 | Viewed by 1477
Abstract
This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ [...] Read more.
This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Comprehensive Quality Assessment Algorithm for Smart Meters
Energies 2019, 12(19), 3690; https://0-doi-org.brum.beds.ac.uk/10.3390/en12193690 - 26 Sep 2019
Cited by 1 | Viewed by 848
Abstract
With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits [...] Read more.
With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits of power utilities. First, seven indexes for assessing operation quality of smart meters are defined based on the metering data and the Gaussian mixture model (GMM) clustering algorithm is applied to extract the typical index data from the massive data of smart meters. Then, the combination optimization model of index’s weight is presented with the subject experience of experts and object difference of data considered; and the comprehensive assessment algorithm based on the revised technique for order preference by similarity to an ideal solution (TOPSIS) is proposed to evaluate the operation quality of smart meters. Finally, the proposed data-driven assessment algorithm is illustrated by the actual metering data from Zhejiang Ningbo power supply company of China and practical application is briefly introduced. The results show that the proposed algorithm is effective for assessing the operation quality of smart meters and could be helpful for energy measurement and asset management. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model
Energies 2019, 12(18), 3540; https://0-doi-org.brum.beds.ac.uk/10.3390/en12183540 - 16 Sep 2019
Cited by 2 | Viewed by 704
Abstract
Timely forecasting of the kick risk after a well kick can reduce the waiting time after well shut-in and provide more time for well killing operations. At present, the multiphase flow model is used to simulate and forecast the pit gain and casing [...] Read more.
Timely forecasting of the kick risk after a well kick can reduce the waiting time after well shut-in and provide more time for well killing operations. At present, the multiphase flow model is used to simulate and forecast the pit gain and casing pressure. Due to the complexity of downhole conditions, calculation of the multiphase flow model is difficult. In this paper, the time series analysis method is used to excavate the information contained in the time-varying data of pit gain and casing pressure. A forecasting model based on a time series analysis method of pit gain and casing pressure is established to forecast the pit gain and casing pressure after a kick. To divide the kick risk level and achieve the forecasting of the kick risk before and after well shut-in, kick risk analysis plates based on pit gain and casing pressure are established. Three pit gain cases and one casing pressure case are studied, and a comparison between measured data and predicted data shows that the proposed method has high prediction accuracy and repeatability. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China
Energies 2019, 12(17), 3258; https://0-doi-org.brum.beds.ac.uk/10.3390/en12173258 - 23 Aug 2019
Cited by 8 | Viewed by 1040
Abstract
Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low [...] Read more.
Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network
Energies 2019, 12(16), 3052; https://0-doi-org.brum.beds.ac.uk/10.3390/en12163052 - 08 Aug 2019
Cited by 12 | Viewed by 1114
Abstract
Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new [...] Read more.
Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Analysis of IEC 61850-9-2LE Measured Values Using a Neural Network
Energies 2019, 12(9), 1618; https://0-doi-org.brum.beds.ac.uk/10.3390/en12091618 - 28 Apr 2019
Cited by 3 | Viewed by 1503
Abstract
Process bus communication has an important role to digitalize substations. The IEC 61850-9-2 standard specifies the requirements to transmit digital data over Ethernet networks. The paper analyses the impact of IEC 61850-9-2LE on physical protections with (analog-digital) input data of voltage and current. [...] Read more.
Process bus communication has an important role to digitalize substations. The IEC 61850-9-2 standard specifies the requirements to transmit digital data over Ethernet networks. The paper analyses the impact of IEC 61850-9-2LE on physical protections with (analog-digital) input data of voltage and current. With the increased interaction between physical devices and communication components, the test proposes a communication analysis for a substation with the conventional method (analog input) and digital method based on the IEC 61850 standard. The use of IEC 61850 as the basis for smart grids includes the use of merging units (MUs) and deployment of relays based on microprocessors. The paper analyses the merging unit’s functions for relays using IEC 61850-9-2LE. The proposed method defines the sampled measured values source and analysis of the traffic. By using neural net pattern recognition that solves the pattern recognition problem, a relation between the inputs (number of samples/ms—interval time between the packets) and the source of the data is found. The benefit of this approach is to reduce the time to test the merging unit by getting the feedback from the merging unit and using the neural network to get the data structure of the publisher IED. Tests examine the GOOSE message and performance using the IEC standard based on a network traffic perspective. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis
Energies 2019, 12(4), 617; https://0-doi-org.brum.beds.ac.uk/10.3390/en12040617 - 15 Feb 2019
Cited by 7 | Viewed by 1229
Abstract
This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering [...] Read more.
This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Interannual Variation in Night-Time Light Radiance Predicts Changes in National Electricity Consumption Conditional on Income-Level and Region
Energies 2019, 12(3), 456; https://0-doi-org.brum.beds.ac.uk/10.3390/en12030456 - 31 Jan 2019
Cited by 11 | Viewed by 1770
Abstract
Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what [...] Read more.
Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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Article
Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation
Energies 2018, 11(9), 2268; https://0-doi-org.brum.beds.ac.uk/10.3390/en11092268 - 29 Aug 2018
Cited by 5 | Viewed by 1209
Abstract
We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of [...] Read more.
We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources. Full article
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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