Applications of Artificial Neural Networks With Input and output Degradation data for Renewable Energy Systems Fault Prognosis

2021 ◽  
Author(s):  
Arij Nasfia Hayder ◽  
Lotfi Saidi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
Degradation Data ◽  
Input And Output ◽  
Fault Prognosis ◽  
Artificial Neural

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

2021 ◽  
Author(s):  
özlem karadag albayrak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Moving Average ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Ann Model ◽  
Artificial Neural ◽  
Renewable Energy Generation

Abstract Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.

Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Jane Oktavia Kamadinata ◽  
Tan Lit Ken ◽  
Tohru Suwa
Keyword(s):  
Neural Networks ◽  
Power Generation ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Solar Irradiance ◽  
Movement Direction ◽  
Simple Method ◽  
Photovoltaic Power ◽  
Sampling Points ◽  
Artificial Neural

Abstract Renewable energy is an attractive alternative source of energy to fossil fuels, as it can help prevent global warming and air pollution. Solar energy, one of the most promising renewable energy sources, can be converted into electricity using photovoltaic power generation systems. Anywhere on the Earth, solar irradiance generally fluctuates during the day but depends on atmospheric conditions. Thus, when a photovoltaic power generation system is connected to a conventional electricity network, predicting near-future global solar irradiance, especially its drastic increases and decreases, is critical to stabilize the network. In this research, a simple method utilizing artificial neural networks to predict large increases and decreases in global solar irradiance is developed. The red–blue ratio (RBR) values, which are extracted from a set of sampling points in images of the sky, as well as the corresponding global solar irradiance values, are used as the artificial neural network inputs. The direction of the movement of clouds is predicted using RBR data at the sampling points. Then, solar irradiance is predicted using the RBR values along the axis closest to the predicted cloud movement direction and the corresponding solar irradiance measurements. The proposed methodology is able to predict both large increases and decreases in solar irradiance greater than 50 through 100 W/m2 1 min in advance with a 40% prediction error. A significant reduction in computational effort is achieved compared to existing sky image-based methodologies using limited sky image data.

Investigating of Correlation between MODIS Satellite Data and Ground Data Related to Aerosols and Modeling Aerosols Using Neural Networks over Shiraz

2012 ◽  
Vol 260-261 ◽  
pp. 926-929
Author(s):  
Ali Reza Dehghani ◽  
Ali Akbar Safavi ◽  
Mohammad Jafar Nazemossadat ◽  
Seyed Mohammad Hessam Mohammadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Satellite Data ◽  
Satellite Image ◽  
Frequency Bands ◽  
Input And Output ◽  
Satellite Information ◽  
Proposed Model ◽  
Artificial Neural ◽  
Band Number

This paper presents an investigation of satellite data and ground data about aerosols and then modelsthe mentioned data over Shiraz using artificial neural networks. MODIS satellite data are available on 36 various frequency bands. In this study, a good correlation between ground data and the 10 first satellite image bands is being shown. Specially, the best correlation was found in band number 8. Therefore, using neural networks and ground data along with satellite information, a model of aerosols is constructed. In the mentioned model, satellite data of band 8 and ground data are used as network input and output, respectively. The results show the effectiveness of the proposed model.

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.

Incident detection and classification in renewable energy news using pre-trained language models on deep neural networks

2021 ◽  
pp. 1-20
Author(s):  
Qiqing Wang ◽  
Cunbin Li
Keyword(s):  
Neural Networks ◽  
Renewable Energy ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Energy Systems ◽  
Language Models ◽  
Causal Effects ◽  
Incident Detection ◽  
Renewable Energy Systems ◽  
Learning Framework

The surge of renewable energy systems can lead to increasing incidents that negatively impact economics and society, rendering incident detection paramount to understand the mechanism and range of those impacts. In this paper, a deep learning framework is proposed to detect renewable energy incidents from news articles containing accidents in various renewable energy systems. The pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) and word2vec are utilized to represent textual inputs, which are trained by the Text Convolutional Neural Networks (TCNNs) and Text Recurrent Neural Networks. Two types of classifiers for incident detection are trained and tested in this paper, one is a binary classifier for detecting the existence of an incident, the other is a multi-label classifier for identifying different incident attributes such as causal-effects and consequences, etc. The proposed incident detection framework is implemented on a hand-annotated dataset with 5 190 records. The results show that the proposed framework performs well on both the incident existence detection task (F1-score 91.4%) and the incident attributes identification task (micro F1-score 81.7%). It is also shown that the BERT-based TCNNs are effective and robust in detecting renewable energy incidents from large-scale textual materials.

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