Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Artificial Neural Networks in Smart Grids
share announcement

Artificial Neural Networks in Smart Grids

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 March 2020) | Viewed by 31317

Special Issue Editor

Dr. Nikolaos Paterakis

E-Mail Website
Guest Editor
Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Interests: electricity markets; machine learning; optimization; power system operations and planning; renewable energy; Smart Grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past several decades, the proliferation of monitoring, control, and communication infrastructure has been at the forefront of innovation in electrical energy systems. As a result, unprecedentedly large amounts of data pertaining to the generation, transmission, and consumption of electricity have become available. Leveraging these data streams to produce advanced data analytics may arguably facilitate the transition from the current grid to an interoperable Smart Grid with enhanced operational, planning, and economic efficiency.

Recent advances in machine learning have led to the adoption of statistical methods in the execution of tasks, without explicitly considering physical models of the underlying system. Among the most celebrated machine learning models, artificial neural networks (ANNs) present the significant advantages of being able to learn complex non-linear relationships between input variables and output targets, as well as generalizing the relationships that they have learned on unseen data. Especially, the recent success of deep learning—a subfield of machine learning—in tackling problems in other disciplines has reinforced the interest in applying different ANN architectures to also confronting problems related to Smart Grids driven by an ever-increasing amount of available data.

ANNs have already been applied in various fields related to Smart Grids research, including asset management, energy management systems, forecasting methods, security and reliability assessment, state estimation, and data-driven decision-making systems, to name a few. However, significant challenges concerning information management, data privacy, as well as the vulnerability and robustness of such techniques to malicious data still remain.

In this Special Issue, we invite contributions that aim to expand the application field of artificial neural networks in Smart Grids under all machine learning paradigms (supervised, unsupervised, and reinforcement learning), address existing challenges, and bring forward new problems. Both original research and comprehensive review papers are welcome.

Assist. Prof.  Nikolaos Paterakis
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. Applied Sciences 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 2400 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

  • artificial neural networks
  • deep learning
  • machine learning
  • reinforcement learning
  • Smart Grids

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

32 pages, 2889 KiB  
Article
A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism
by Ping Jiang and Ying Nie
Appl. Sci. 2020, 10(4), 1550; https://0-doi-org.brum.beds.ac.uk/10.3390/app10041550 - 24 Feb 2020
Cited by 7 | Viewed by 2369
Abstract
Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power [...] Read more.
Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

17 pages, 1379 KiB  
Article
Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification
by Luca Massidda, Marino Marrocu and Simone Manca
Appl. Sci. 2020, 10(4), 1454; https://0-doi-org.brum.beds.ac.uk/10.3390/app10041454 - 21 Feb 2020
Cited by 62 | Viewed by 5056
Abstract
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep [...] Read more.
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

18 pages, 585 KiB  
Article
Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models
by Sumeyra Demir, Krystof Mincev, Koen Kok and Nikolaos G. Paterakis
Appl. Sci. 2020, 10(1), 255; https://0-doi-org.brum.beds.ac.uk/10.3390/app10010255 - 28 Dec 2019
Cited by 17 | Viewed by 4053
Abstract
Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features [...] Read more.
Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features capable of capturing the effects of technical or fundamental price drivers must be identified. In this paper, we focus on identifying/engineering technical features capable of capturing the behavioural biases of DAM traders. Technical indicators (TIs), such as Bollinger Bands, Momentum indicators, or exponential moving averages, are widely used across financial markets to identify behavioural biases. To date, TIs have never been applied to the forecasting of DAM prices. We demonstrate how the simple inclusion of TI features in DAM forecasting can significantly boost the regression accuracies of machine learning models; reducing the root mean squared errors of linear, ensemble, and deep model forecasts by up to 4.50%, 5.42%, and 4.09%, respectively. Moreover, tailored TIs are identified for each of these models, highlighting the added explanatory power offered by technical features. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

18 pages, 936 KiB  
Article
Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
by Sana Mujeeb, Turki Ali Alghamdi, Sameeh Ullah, Aisha Fatima, Nadeem Javaid and Tanzila Saba
Appl. Sci. 2019, 9(20), 4417; https://0-doi-org.brum.beds.ac.uk/10.3390/app9204417 - 18 Oct 2019
Cited by 71 | Viewed by 5827
Abstract
Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial [...] Read more.
Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

16 pages, 2555 KiB  
Article
Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine
by Jidong Wang, Zhilin Xu and Yanbo Che
Appl. Sci. 2019, 9(11), 2315; https://0-doi-org.brum.beds.ac.uk/10.3390/app9112315 - 05 Jun 2019
Cited by 25 | Viewed by 3026
Abstract
In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification [...] Read more.
In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 963 KiB  
Review
A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources
by Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Fernando Olivencia Polo and Adolfo Crespo Márquez
Appl. Sci. 2019, 9(9), 1844; https://0-doi-org.brum.beds.ac.uk/10.3390/app9091844 - 05 May 2019
Cited by 129 | Viewed by 8927
Abstract
The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that [...] Read more.
The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis. Full article
(This article belongs to the Special Issue Artificial Neural Networks in Smart Grids)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop