Forecasting of solar irradiance for solar power plants by artificial neural network

2015 ◽  
Author(s):  
S. Watetakarn ◽  
S. Premrudeepreechacharn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Irradiance ◽  
Power Plants ◽  
Solar Power ◽  
Solar Power Plants ◽  
Artificial Neural

scholarly journals A neural network model for short-term forecasting of electricity generation by solar power plants

2020 ◽  
pp. 145-158
Author(s):  
Dmitry Tyunkov ◽  
◽  
Alexander Gritsay ◽  
Alina Sapilova ◽  
Alexandr Blokhin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Data ◽  
Predictive Model ◽  
Power Plants ◽  
Solar Power ◽  
Energy Sources ◽  
Short Term ◽  
Solar Power Plants ◽  
Short Term Forecasting

Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

scholarly journals Towards the Development of a Low-Cost Irradiance Nowcasting Sky Imager

2019 ◽  
Vol 9 (6) ◽  
pp. 1131 ◽  
Author(s):  
Luis Valentín ◽  
Manuel Peña-Cruz ◽  
Daniela Moctezuma ◽  
Cesar Peña-Martínez ◽  
Carlos Pineda-Arellano ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Power Plants ◽  
Solar Power ◽  
Detection System ◽  
Low Cost ◽  
Resource Assessment ◽  
The Sun ◽  
Robust Detection ◽  
Solar Power Plants ◽  
Solar Resource

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.

Artificial Neural Network Model for Diagnosing the Performance and the Conditions of Air-Operated Valves

2020 ◽  
Author(s):  
Taeyun Kim ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Ilyoung Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Physical Models ◽  
Artificial Neural

Abstract Air-operated valves (AOVs) are used to control or shut off the flow in the nuclear power plants. In particular, the failure of safety-related AOV could have significant impacts on the safety of the nuclear power plants and therefore, their performances have been tested and evaluated periodically. However, the current method to evaluate the performance needs to be revised to enhance the accuracy and to identify defects of AOV independently of personal skills. This paper introduce the ANN (Artificial Neural Network) model to diagnose the performance and the condition altogether. Test facilities were designed and configured to measure the signals such as supply pressure, control pressure, actuator pressure, stem displacement and stem thrust. Tests were carried out in various conditions which simulate defects with leak/clogged pipes, the bent stem and so on. First, the physical models of an AOV are developed to describe its behavior and to parameterize the characteristics of each component for evaluating the performance. Secondly, CNN (Convolutional Neural Network) architectures are designed considering the developed physical models to make a lead to the optimal performance of ANN. To train the ANN effectively, the measured signals were divided into several regions, from each of which the features are extracted and the extracted features are combined for classifying the defects. In addition, the model can provide the parameters of maximum available thrust, which is the key factor in periodic verification of AOV with the required accuracy and classify more than 10 different kinds of defects with high accuracy.

scholarly journals Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

2021 ◽  
Vol 54 (6) ◽  
pp. 891-895
Author(s):  
Fawaz S. Abdullah ◽  
Ali N. Hamoodi ◽  
Rasha A. Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Machine Learning Algorithms ◽  
Circuit Board ◽  
Electric Circuits ◽  
The Neural Network ◽  
Artificial Neural

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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