Special Issue "Applications of Artificial Neural Networks for Energy Systems"

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

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editor

Guest Editor
Prof. Giuseppe Marco TINA

Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania. A.Doria, n. 6 - 95125 Catania, Italy
Website | E-Mail
Interests: MATLAB Simulation, Renewable Energy generating systems, Electrical Power Engineering, diagnostic and monitoring of solar systems

Special Issue Information

Dear Colleagues,

Artificial neural networks (ANNs) are a feasible way to deal with complex and ill-defined problems. ANNs are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to tackle non-linear problems, and once trained, based on examples and historical data, can perform very rapidly predictions and generalizations.

This Special Issue of Energies will explore the latest developments in applications of ANNs for energy systems.

The spectrum of applications is very large, so we would like to focus on, though not exclusively, on applications to power systems.

We would, particularly, welcome those that offer insights about forecast of demand and production, mainly of renewable non programmable energy sources. This Special Issue will include, but not be limited, the use on ANNSs in the following subjects:

  • modeling, performance estimation, forecast and diagnostic of PV and wind generators;

  • modeling and forecasting electricity demand;

  • forecasting electricity markets prices;

  • bidding strategies of electricity markets participants;

  • balancing issues in power systems;

  • optimal sizing of renewable non programmable systems (e.g. PV, Wind and, Hybrid);

  • energy management systems for microgrids.

Prof. Giuseppe Marco Tina
Guest Editor

Manuscript Submission Information

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Keywords

  • Artificial neural network (ANNs)

  • Power system

  • Solar energy

  • Microgrids

  • Wind energy

  • Electricity demand

  • Electricity markets

  • Balancing

  • Forecast

  • Diagnostic

  • Performance estimation

  • Energy management

Published Papers (12 papers)

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Research

Open AccessArticle Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method
Appl. Sci. 2019, 9(3), 520; https://doi.org/10.3390/app9030520
Received: 15 December 2018 / Revised: 25 January 2019 / Accepted: 29 January 2019 / Published: 3 February 2019
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Abstract
In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy [...] Read more.
In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
Appl. Sci. 2019, 9(1), 209; https://doi.org/10.3390/app9010209
Received: 4 December 2018 / Revised: 27 December 2018 / Accepted: 31 December 2018 / Published: 8 January 2019
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Abstract
In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to [...] Read more.
In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
Appl. Sci. 2019, 9(1), 204; https://doi.org/10.3390/app9010204
Received: 6 December 2018 / Revised: 30 December 2018 / Accepted: 31 December 2018 / Published: 8 January 2019
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Abstract
Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for [...] Read more.
Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
Appl. Sci. 2019, 9(1), 126; https://doi.org/10.3390/app9010126
Received: 28 November 2018 / Revised: 11 December 2018 / Accepted: 19 December 2018 / Published: 1 January 2019
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Abstract
Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, [...] Read more.
Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application
Appl. Sci. 2019, 9(1), 51; https://doi.org/10.3390/app9010051
Received: 22 November 2018 / Revised: 10 December 2018 / Accepted: 19 December 2018 / Published: 24 December 2018
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Abstract
Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable [...] Read more.
Because of their fuel flexibility, Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition. Yet, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. The best simulator obtained is a 3-hidden layer ANN (25/22/18 neurons per layer, hyperbolic tangent sigmoid as transfer function, obtained with a gradient descent with adaptive learning rate backpropagation). This shows high accuracy (RMS = 0.67% in the testing phase) and successful application in the forecast of SOFC polarization behaviour in two additional experiments (RMS in the order of 3% is scored, yet it is reduced to about 2% if only the typical operating current density range of real application is considered, from 300 to 500 mA·cm−2). Therefore, the neural tool is suitable for system simulation codes/software whether SOFC operating parameters agree with the input ranges (anode feeding composition 0–48%vol H2, 0–38%vol CO, 0–45%vol CH4, 9–32%vol CO2, 0–54%vol N2, specific equivalent hydrogen flow-rate per unit cell active area 10.8–23.6 mL·min−1·cm−2, current density 0–1300 mA·cm−2 and temperature 700–800 °C). Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Adaptive Solar Power Forecasting based on Machine Learning Methods
Appl. Sci. 2018, 8(11), 2224; https://doi.org/10.3390/app8112224
Received: 19 September 2018 / Revised: 20 October 2018 / Accepted: 7 November 2018 / Published: 12 November 2018
Cited by 1 | PDF Full-text (716 KB) | HTML Full-text | XML Full-text
Abstract
Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for [...] Read more.
Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems
Appl. Sci. 2018, 8(11), 2185; https://doi.org/10.3390/app8112185
Received: 16 October 2018 / Revised: 1 November 2018 / Accepted: 2 November 2018 / Published: 7 November 2018
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Abstract
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep [...] Read more.
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the China Southern Power Grid. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery
Appl. Sci. 2018, 8(11), 2078; https://doi.org/10.3390/app8112078
Received: 28 September 2018 / Revised: 20 October 2018 / Accepted: 25 October 2018 / Published: 28 October 2018
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Abstract
On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite [...] Read more.
On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Artificial Neural Networks as Metamodels for the Multiobjective Optimization of Biobutanol Production
Appl. Sci. 2018, 8(6), 961; https://doi.org/10.3390/app8060961
Received: 29 March 2018 / Revised: 1 June 2018 / Accepted: 1 June 2018 / Published: 12 June 2018
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Abstract
Process optimization using a physical process or its comprehensive model often requires a significant amount of time. To remedy this problem, metamodels, or surrogate models, can be used. In this investigation, a methodology for optimizing the biobutanol production process via the integrated acetone–butanol–ethanol [...] Read more.
Process optimization using a physical process or its comprehensive model often requires a significant amount of time. To remedy this problem, metamodels, or surrogate models, can be used. In this investigation, a methodology for optimizing the biobutanol production process via the integrated acetone–butanol–ethanol (ABE) fermentation–membrane pervaporation process is proposed. In this investigation, artificial neural networks (ANNs) were used as metamodels in an attempt to reduce the time needed to circumscribe the Pareto domain and identify the best optimal operating conditions. Two different metamodels were derived from a small set of operating conditions obtained from a uniform experimental design. The first series of metamodels were derived to entirely replace the phenomenological model of the butanol fermentation process by representing the relationship that exists between five operating conditions and four performance criteria. The second series of metamodels were derived to estimate the initial concentrations under steady-state conditions for the eight chemical species within the fermenter in order to expedite convergence of the process simulator. The first series of metamodels led to an accurate Pareto domain and reduced the computation time to circumscribe the Pareto domain by a factor of 2500. The second series of metamodels led to only a small reduction of computation time (a factor of approximately 2) because of the inherently slow convergence of the overall fermentation process. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks
Appl. Sci. 2018, 8(5), 749; https://doi.org/10.3390/app8050749
Received: 7 February 2018 / Revised: 16 April 2018 / Accepted: 26 April 2018 / Published: 9 May 2018
Cited by 1 | PDF Full-text (1786 KB) | HTML Full-text | XML Full-text
Abstract
Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify [...] Read more.
Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify their power supply by including renewable wind, hydro and photovoltaic energy sources. The integration and optimization of the multiple energy sources requires a design of a new power management system that can anticipate how much electricity will be drawn by the community and how much electricity will be generated from the renewable sources in order to control the fossil fuel-based generators to meet the community power demand. To address this problem, we designed a hybrid machine learning algorithm to predict community power draw as one module for the next generation microgrid power management system. The algorithm divides the task of a power load prediction into two sub-models: the first temporal model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the community power demand. The results illustrate (1) the feasibility of building a machine learning algorithm that uses relatively small data for model training and validation, (2) the ability to predict the near-future community power load for the microgrids operating in the environments with highly dynamic weather conditions and (3) how the integration of multiple low quality future weather conditions produced high accuracy power load prediction. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Predicting Output Power for Nearshore Wave Energy Harvesting
Appl. Sci. 2018, 8(4), 566; https://doi.org/10.3390/app8040566
Received: 22 February 2018 / Revised: 15 March 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
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Abstract
Energy harvested from a Wave Energy Converter (WEC) varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power [...] Read more.
Energy harvested from a Wave Energy Converter (WEC) varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Open AccessArticle Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting
Appl. Sci. 2017, 7(11), 1142; https://doi.org/10.3390/app7111142
Received: 13 October 2017 / Revised: 26 October 2017 / Accepted: 30 October 2017 / Published: 6 November 2017
Cited by 2 | PDF Full-text (2297 KB) | HTML Full-text | XML Full-text
Abstract
The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind [...] Read more.
The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naïve approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy. Full article
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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