Solar Energy Prediction Using Backpropagation in Artificial Neural Networks

2018 ◽  
pp. 27-34
Author(s):  
Abhishek Kumar ◽  
Ravi Khatri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Energy Prediction ◽  
Artificial Neural

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

2015 ◽  
Vol 34 (5) ◽  
pp. 1528-1535
Author(s):  
Ali Wadi Abbas Al-Fatlawi ◽  
Nasrudin Abdul Rahim ◽  
Rahman Saidur ◽  
Thomas Arthur Ward
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Peninsular Malaysia ◽  
Complex Topography ◽  
Energy Prediction ◽  
Artificial Neural

scholarly journals Solar Energy Prediction for Malaysia Using Artificial Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Clearness Index ◽  
Energy Prediction ◽  
Global Solar Irradiation ◽  
Artificial Neural

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

A New Approach for Meteorological Variables Prediction at Kuala Lumpur, Malaysia, Using Artificial Neural Networks: Application for Sizing and Maintaining Photovoltaic Systems

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
M. Mahmoud ◽  
K. Sopian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Energy ◽  
Ambient Temperature ◽  
Meteorological Variables ◽  
Mean Bias Error ◽  
Artificial Neural ◽  
Prediction Approach

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

scholarly journals Comparison of Energy Prediction Algorithms for Differential and Skid-Steer Drive Mobile Robots on Different Ground Surfaces

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6722
Author(s):  
Krystian Góra ◽  
Mateusz Kujawinski ◽  
Damian Wroński ◽  
Grzegorz Granosik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mobile Robots ◽  
Mobile Robotics ◽  
Analytical Models ◽  
Literature Analysis ◽  
Kinematic Structure ◽  
Energy Prediction ◽  
Artificial Neural ◽  
Complex Knowledge

A detailed literature analysis depicts that artificial neural networks are rarely used for the power consumption estimation in the mobile robotics field. Instead, researchers prefer to develop analytical models of investigated robots. This manuscript presents a comparison of mathematical models and non-complex artificial neural networks in energy prediction tasks for differential and skid-steer drive robots which move over various types of surfaces. The results show that both methods could be used interchangeably but AI methods are more universal, do not depend on the kinematic structure of a robot and are tolerant for designers not having a complex knowledge about the system.

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