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A platform for research: civil engineering, architecture and urbanism
Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output
The efficiency, return on investment, and output of Solar power plants are all significantly impacted by the development of Digital Twin technology. Digital Twining of the Solar PV system will help to improve the predictability of the power output, gather the necessary information for energy forecasting (predict ahead the power output to balance supply and demand) and power system default detection. The main objective of this study is to develop a Digital Twin Solar PV system. The technical approach used to develop a Solar PV Digital Twin includes: (1) real time data collection of power output from Solar PV system using smart sensors, (2) development, calibration, and validation of a Solar PV system model using MATLAB (3) development and testing the accuracy of LSTM-based forecasting model to predict ahead (15 mins) the Solar PV electrical power output using historical data. The results show that the Solar PV Digital Twin compares well with the targeted data from the real system (data from sensors of the physical Solar PV system). The accuracy of the models was assessed using the coefficient of correlation (R). The results show that the Solar PV Digital Twin simulation and forecasting models have R-values of 0.99893 and 0.99427, respectively. To help an asset owner or operator to visualize the performance of Digital Twin, a dashboard application was developed using MATLAB App Designer. The dashboard shows current, historical, and continuously forecasts future energy consumption and production to balance supply and demand. The development of Digital Twin technology, artificial intelligence and machine learning predictive tools for monitoring, forecasting and maintenance of Solar PV plants are keys to reach the Net Zero Energy Building goals. Additionally, to ensure the reproducibility of the research, the irradiance measurements data was published in a GitHub repository.
Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output
The efficiency, return on investment, and output of Solar power plants are all significantly impacted by the development of Digital Twin technology. Digital Twining of the Solar PV system will help to improve the predictability of the power output, gather the necessary information for energy forecasting (predict ahead the power output to balance supply and demand) and power system default detection. The main objective of this study is to develop a Digital Twin Solar PV system. The technical approach used to develop a Solar PV Digital Twin includes: (1) real time data collection of power output from Solar PV system using smart sensors, (2) development, calibration, and validation of a Solar PV system model using MATLAB (3) development and testing the accuracy of LSTM-based forecasting model to predict ahead (15 mins) the Solar PV electrical power output using historical data. The results show that the Solar PV Digital Twin compares well with the targeted data from the real system (data from sensors of the physical Solar PV system). The accuracy of the models was assessed using the coefficient of correlation (R). The results show that the Solar PV Digital Twin simulation and forecasting models have R-values of 0.99893 and 0.99427, respectively. To help an asset owner or operator to visualize the performance of Digital Twin, a dashboard application was developed using MATLAB App Designer. The dashboard shows current, historical, and continuously forecasts future energy consumption and production to balance supply and demand. The development of Digital Twin technology, artificial intelligence and machine learning predictive tools for monitoring, forecasting and maintenance of Solar PV plants are keys to reach the Net Zero Energy Building goals. Additionally, to ensure the reproducibility of the research, the irradiance measurements data was published in a GitHub repository.
Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output
Al-Isawi, Omar Adil (author) / Amirah, Lutfi Hatem (author) / Al-Mufti, Omar Ahmed (author) / Ghenai, Chaouki (author)
2023-02-20
3698226 byte
Conference paper
Electronic Resource
English
| Elsevier | 2025