Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant

2016 ◽  
Vol 4 (4) ◽  
pp. 20-32 ◽  
Author(s):  
Rafik Fainti ◽  
Antonia Nasiakou ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Output ◽  
Power Plants ◽  
Ambient Pressure ◽  
Combined Cycle ◽  
Frequent Use ◽  
Power Generation System ◽  
Artificial Neural ◽  
Increasing Demand

The increasing demand for electricity the last decades leads towards the more frequent use of Combined Cycle Power Plants (CCPPs) because of the quite efficient way these units are capable to produce electricity. Hence, the prediction of the output of these units is of significant interest and constitutes the cornerstone towards the attainment of economic power production and a reliable power generation system as a whole. To that end, the aim of this paper is the development of a hierarchical predictive method based on Artificial Neural Networks (ANNs) in order to efficiently predict the power plant's output. The under consideration features are the hourly average ambient variables of Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) for predicting the hourly power output of a CCPP. A parellel, but equally important, aim of this study is to assess the effectiveness of ANNs in this type of applications.

Hierarchical Method Based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant

2020 ◽  
pp. 109-122
Author(s):  
Rafik Fainti ◽  
Antonia Nasiakou ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Output ◽  
Power Plants ◽  
Ambient Pressure ◽  
Combined Cycle ◽  
Frequent Use ◽  
Power Generation System ◽  
Artificial Neural ◽  
Increasing Demand

The increasing demand for electricity the last decades leads towards the more frequent use of Combined Cycle Power Plants (CCPPs) because of the quite efficient way these units are capable to produce electricity. Hence, the prediction of the output of these units is of significant interest and constitutes the cornerstone towards the attainment of economic power production and a reliable power generation system as a whole. To that end, the aim of this paper is the development of a hierarchical predictive method based on Artificial Neural Networks (ANNs) in order to efficiently predict the power plant's output. The under consideration features are the hourly average ambient variables of Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) for predicting the hourly power output of a CCPP. A parellel, but equally important, aim of this study is to assess the effectiveness of ANNs in this type of applications.

Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks

2021 ◽  
Author(s):  
Oleksandr Savchenko ◽  
Oleksandr Miroshnyk ◽  
Oleksandr Moroz ◽  
Iryna Trunova ◽  
Anatolii Sereda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Radiation ◽  
Power Plants ◽  
Solar Power ◽  
Solar Power Plants ◽  
Artificial Neural

Predicting Archimedes Screw Generator Power Output Using Artificial Neural Networks

2018 ◽  
Vol 144 (3) ◽  
pp. 05018002 ◽  
Author(s):  
Andrew Kozyn ◽  
Kathleen Songin ◽  
Bahram Gharabaghi ◽  
William David Lubitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Output ◽  
Artificial Neural

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks

2003 ◽  
Author(s):  
Pernilla Olausson ◽  
Daniel Ha¨ggsta˚hl ◽  
Jaime Arriagada ◽  
Erik Dahlquist ◽  
Mohsen Assadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Hybrid Model ◽  
Power Plants ◽  
Computational Time ◽  
Process Identification ◽  
Calculation Time ◽  
Artificial Neural

Traditionally, when process identification, monitoring and diagnostics are carried out for power plants and engines, physical modeling such as heat and mass balances, gas path analysis, etc. is utilized to keep track of the process. This type of modeling both requires and provides considerable knowledge of the process. However, if high accuracy of the model is required, this is achieved at the expense of computational time. By introducing statistical methods such as Artificial Neural Networks (ANNs), the accuracy of the complex model can be maintained while the calculation time is often reduced significantly reduced. The ANN method has proven to be a fast and reliable tool for process identification, but the step from the traditional physical model to a pure ANN model is perhaps too wide and, in some cases, perhaps unnecessary also. In this work, the Evaporative Gas Turbine (EvGT) plant was modeled using both physical relationships and ANNs, to end up with a hybrid model. The type of architecture used for the ANNs was the feed-forward, multi-layer neural network. The main objective of this study was to evaluate the viability, the benefits and the drawbacks of this hybrid model compared to the traditional approach. The results of the case study have clearly shown that the hybrid model is preferable. Both the traditional and the hybrid models have been verified using measured data from an existing pilot plant. The case study also shows the simplicity of integrating an ANN into conventional heat and mass balance software, already implemented in many control systems for power plants. The access to a reliable and faster hybrid model will ultimately give more reliable operation, and ultimately the lifetime profitability of the plant will be increased. It is also worth mentioning that for diagnostic purposes, where advanced modeling is important, the hybrid model with calculation time well below one second could be used to advantage in model predictive control (MPC).

Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

2004 ◽  
Vol 160 (1-4) ◽  
pp. 207-224 ◽  
Author(s):  
Young Joon Choi ◽  
Hyun Koon Kim ◽  
Won Pil Baek ◽  
Soon Heung Chang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Simulation Methodology ◽  
Accident Simulation ◽  
Artificial Neural
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