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.

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

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

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.

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.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks

2012 ◽  
Vol 532-533 ◽  
pp. 1036-1040
Author(s):  
Jian Qing Liu ◽  
Mei Luo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Information Filtering ◽  
Monitor System ◽  
Filtering Method ◽  
Media Attention ◽  
Text Information ◽  
Novel Method ◽  
Artificial Neural

Internet monitoring has recently been the focus of media attention and public debate. This paper proposed a novel method of multi-layer smart monitor system to filter unhealthy information using artificial neural networks (ANN). This method classified the text into multilayer and uses RPROP algorithm to implement the text classifier. Finally, the test was deployed and the feasibility of this algorithm was proven.

Indirect aircraft structural monitoring using artificial neural networks

2008 ◽  
Vol 112 (1131) ◽  
pp. 251-265 ◽  
Author(s):  
S. C. Reed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Cost Effective ◽  
Military Aircraft ◽  
Royal Air Force ◽  
Parametric Data ◽  
Flight Parameters ◽  
Artificial Neural

Abstract From necessity, military aircraft often operate in a highly fatigue damaging environment and history has shown in lost lives and aircraft the consequences of failure to appreciate fully the usage environment. The need for robust and cost effective structural usage monitoring of military aircraft to ensure operations are conducted within acceptable levels of risk is paramount. Furthermore, increased economic pressures require ever-inventive methods to be employed to maximise the lives of military fleets; structural usage monitoring will be a key asset in this drive. A highly cost effective indirect structural health and usage neural network (SHAUNN) monitoring system is proposed. A SHAUNN uses regression relationships determined by artificial neural networks to predict stresses, strains, loads, or fatigue damage from flight parameters. Within this paper the development of a SHAUNN monitoring system is described. Flight parametric data, captured during Operational Loads Measurement of the Royal Air Force Dominie TMk1 aircraft have been used to predict stresses at the key structural location in the wing, using mapping relationships determined by artificial neural networks. A framework for the development of the SHAUNN monitoring system is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. It is concluded that this technology could provide the basis for an accurate, cost-effective structural usage monitoring system and further work to investigate the prediction of ground – based stresses in the wing is recommended.

scholarly journals Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Ashraf A. A. Beshr ◽  
Fawzi H. Zarzoura
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Highway Bridges ◽  
Structural Deformation ◽  
Highway Bridge ◽  
Displacement Prediction ◽  
Adequate Model ◽  
Artificial Neural ◽  
Gnss Observations

AbstractBridges are playing a major role in the socio-economic development of any country over the world. Suspension highway bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these structures is important and vital. One of the main objectives of monitoring the structural deformation is predicting the deformation values, which will help to avoid sudden failure and accidents in the future. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have proven successful solution in many engineering applications and problems. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and displacement prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive model for analysis of GNSS observations and displacement prediction. Several models are applied and compared for prediction of suspension bridge displacement for both kinematic and dynamic models. The resulting predicted displacement values by applying artificial neural networks (ANNs) and ANFIS provide a significant improvement for predicting the structure deformation values for suspension highway bridges from GNSS observations.

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