An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data
In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved,...
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MDPI AG
2021
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oai:doaj.org-article:08e8e42e923d471383cf2f4c513c16b92021-11-11T19:35:16ZAn Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data10.3390/su1321118932071-1050https://doaj.org/article/08e8e42e923d471383cf2f4c513c16b92021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11893https://doaj.org/toc/2071-1050In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10<sup>−7</sup> to 3.19 × 10<sup>−10</sup>. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.Abdul Rauf BhattiAhmed Bilal AwanWalied AlharbiZainal SalamAbdullah S. Bin HumaydPraveen R. P.Kankar BhattacharyaMDPI AGarticlesolar energypower system operationphotovoltaicsPV power predictionArtificial Neural Network (ANN)power forecastingEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11893, p 11893 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
solar energy power system operation photovoltaics PV power prediction Artificial Neural Network (ANN) power forecasting Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
| spellingShingle |
solar energy power system operation photovoltaics PV power prediction Artificial Neural Network (ANN) power forecasting Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Abdul Rauf Bhatti Ahmed Bilal Awan Walied Alharbi Zainal Salam Abdullah S. Bin Humayd Praveen R. P. Kankar Bhattacharya An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| description |
In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10<sup>−7</sup> to 3.19 × 10<sup>−10</sup>. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation. |
| format |
article |
| author |
Abdul Rauf Bhatti Ahmed Bilal Awan Walied Alharbi Zainal Salam Abdullah S. Bin Humayd Praveen R. P. Kankar Bhattacharya |
| author_facet |
Abdul Rauf Bhatti Ahmed Bilal Awan Walied Alharbi Zainal Salam Abdullah S. Bin Humayd Praveen R. P. Kankar Bhattacharya |
| author_sort |
Abdul Rauf Bhatti |
| title |
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| title_short |
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| title_full |
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| title_fullStr |
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| title_full_unstemmed |
An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data |
| title_sort |
improved approach to enhance training performance of ann and the prediction of pv power for any time-span without the presence of real-time weather data |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/08e8e42e923d471383cf2f4c513c16b9 |
| work_keys_str_mv |
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| _version_ |
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