Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms
| Autor: | J. Yogapriya, Vinayagam Mohanavel, R. Kabilan, Robbi Rahim, S. Manoharan, Alagar Karthick, Priyesh P. Gandhi, V. Chandran |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
Computer science 020209 energy TJ807-830 02 engineering and technology Machine learning computer.software_genre Renewable energy sources Flat roof 0202 electrical engineering electronic engineering information engineering General Materials Science Cluster analysis Roof Artificial neural network Renewable Energy Sustainability and the Environment business.industry Photovoltaic system General Chemistry 021001 nanoscience & nanotechnology Atomic and Molecular Physics and Optics Tree (data structure) Facade Artificial intelligence Building-integrated photovoltaics 0210 nano-technology business computer Algorithm |
| Zdroj: | International Journal of Photoenergy, Vol 2021 (2021) |
| Popis: | One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|