scholarly journals 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

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

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.

scholarly journals A novel artificial neural network for power quality improvement in AC microgrid

2021 ◽  
Vol 12 (4) ◽  
pp. 2151
Author(s):  
Debani Prasad Mishra ◽  
Amba Subhadarshini Nayak ◽  
Truptasha Tripathy ◽  
Surender Reddy Salkuti ◽  
Sanhita Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Power Quality ◽  
Power Supply ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Sources ◽  
Artificial Neural

The microgrid concept provides a flexible power supply to the utility where the conventional grid is unable to supply. The microgrid structure is based on renewable energy sources known as distributed generators (DGs) and the power network. Nevertheless, the power quality (PQ) is a great challenge in the microgrid concept. Particularly the inclusion of renewable energy sources into the conventional grids increases the problems in the quality of power, like voltage sag/swell, oscillatory transient, voltage flickering, and voltage notching which reduces the quality and reliability of the power supply. In this paper, a microgrid is considered which consists of PV cells as DG, battery energy storage system (BESS), and a novel control strategy known as the nonlinear autoregressive exogenous model (NARX). The proposed controller is an improved artificial neural network (ANN). The various case studies like sag/swell, unbalanced condition, and voltage deviation have been simulated with the model. The comprehensive simulation results are compared with the proportional-integral (PI) controller. Hence in this paper, the robustness of the proposed controller has been studied through different situations.

scholarly journals Prediction of Day Ahead Hourly Solar Radiation by Meteorological Forecasting Supported Artificial Neural Network: A Case Study for Trabzon Province

2018 ◽  
pp. 87-92
Author(s):  
Sibel Cevik ◽  
Recep Cakmak ◽  
Ismail Hakki Altas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Solar Radiation ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Accurate Estimation ◽  
The Mean ◽  
Artificial Neural

Electricity generation from renewable energy sources is increased day by day. Accurate estimation of electricity generation from the renewable energy sources which have intermittent and variable characteristics is a requirement to ensure stable operation of the electrical grid. In this study, a multi-layer artificial neural network (ANN) system, which is supported by meteorological forecasting data, has been proposed to predict day ahead hourly solar radiation. In this context, the ANN system which operates by based on cause-effect relationship has been designed. In order to increase accuracy of the solar radiation prediction of the designed ANN, a similar day selection algorithm has been developed. A unique ANN has been constituted for each season by evaluating the seasons within itself. The designed ANN model has been designed, trained and tested in MATLAB simulation environment without using codes of the MATLAB ANN toolbox. Day ahead hourly solar radiation of Trabzon province has been predicted by the proposed ANN. The accuracy of the predictions has been evaluated by the mean absolute percentage error (MAPE), the root means squared error (RMSE), the mean absolute error (MAE) and the correlation coefficient (r) performance measures.

scholarly journals Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6405
Author(s):  
Muhammad Ahsan Zamee ◽  
Dongjun Won
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Gradient Descent ◽  
Renewable Energy Sources ◽  
Population Based ◽  
Energy Sources ◽  
Forecasting Model ◽  
Dynamic Learning ◽  
Artificial Neural

A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

scholarly journals Forecasting the Structure of Energy Production from Renewable Energy Sources and Biofuels in Poland

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2539 ◽  
Author(s):  
Jarosław Brodny ◽  
Magdalena Tutak ◽  
Saqib Ahmad Saki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Energy Production ◽  
Public Awareness ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Production Volume ◽  
The European Union ◽  
Artificial Neural

The world’s economic development depends on access to cheap energy sources. So far, energy has been obtained mainly from conventional sources like coal, gas and oil. Negative climate changes related to the high emissions of the economy based on the combustion of hydrocarbons and the growing public awareness have made it necessary to look for new ecological energy sources. This condition can be met by renewable energy sources. Both social pressure and international activities force changes in the structure of sources from which energy is produced. This also applies to the European Union countries, including Poland. There are no scientific studies in the area of forecasting energy production from renewable energy sources for Poland. Therefore, it is reasonable to investigate this subject since such a forecast can have a significant impact on investment decisions in the energy sector. At the same time, it must be as reliable as possible. That is why a modern method was used for this purpose, which undoubtedly involves artificial neural networks. The following article presents the results of the analysis of energy production from renewable energy sources in Poland and the forecasts for this production until 2025. Artificial neural networks were used to make the forecast. The analysis covered eight main sources from which this energy is produced in Poland. Based on the production volume since 1990, predicted volumes of renewable energy sources until 2025 were determined. These forecasts were prepared for all studied renewable energy sources. Renewable energy production plans and their share in total energy consumption in Poland were also examined and included in climate plans. The research was carried out using artificial neural networks. The results should be an important source of information on the effects of implementing climate policies in Poland. They should also be utilized to develop action plans to achieve the objectives of the European Green Deal strategy.

scholarly journals Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2216 ◽  
Author(s):  
Ravi Kishore ◽  
Roop Mahajan ◽  
Shashank Priya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Ann Model ◽  
Predicting Performance ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

scholarly journals INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

2020 ◽  
Vol 8 (5) ◽  
pp. 4047-4068
Author(s):  
Mehmet Hakan ÖZDEMİR ◽  
Murat İNCE ◽  
Batin Latif AYLAK ◽  
Okan ORAL ◽  
Mehmet Ali TAŞ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Solar Power ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Renewable energy sources play an essential role in sustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

scholarly journals Energy management in connected and disconnected mode of a photovoltaic system with a battery storage using an artificial neural network technique

2021 ◽  
Vol 23 (1) ◽  
pp. 54
Author(s):  
Ezzitouni Jarmouni ◽  
Ahmed Mouhsen ◽  
Mohammed Lamhammedi ◽  
Zakarya Benizza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Green Energy ◽  
Photovoltaic System ◽  
Control Technique ◽  
Energy Sources ◽  
Electrical Grid ◽  
Artificial Neural

In order to reduce the inconvenience resulting from the use of the traditional energy sources (oil, gas and coal), the integration of renewable energy sources is among the better solutions. With the integration of green energy sources, there are several strategies that can be adopted, including the combination of clean energy sources (solar, wind, and biomass) with each other, or the combination of renewable sources with conventional sources. In this article, we focus on a photovoltaic system allowing the storage of energy in a battery with a coupling to the electrical grid. In order to overcome the problems related to the random operation that accompanies the use of photovoltaic systems, we have developed a control technique based on the use of artificial neural network technology. The complete system was designed and simulated on MATLAB Simulink.

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