Abstract
The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.
Highlights
The development of technologies to harvest renewable forms of energy, such as solar photovoltaics and wind power generators, is one of the key drivers for their implementation in microgrids, interconnected with the grid, to trade generated electricity [1]
This study proposes an improved method for short-term prediction of building-integrated photovoltaic (BIPV) power generation with simple weather forecast data using feature engineering and machine learning
This may imply that the prediction performance for overcast sky conditions does not depend on the model but, rather, the correlation between the input feature and the BIPV power generation
Summary
The development of technologies to harvest renewable forms of energy, such as solar photovoltaics and wind power generators, is one of the key drivers for their implementation in microgrids, interconnected with the grid, to trade generated electricity [1]. The prediction performance of solar irradiation is still not good because of the quality of forecasted weather data [14]. Direct prediction methods employ historical PV power generation data and forecasted weather conditions that generally do not include solar irradiation. Numerous studies have suggested the prediction of PV power output using various prediction algorithms and hybrid models based on the direct method, conventional direct methods have limited ability to maintain a high hourly prediction performance of short-term PV power output because these models mainly depend on forecasted weather data, which does not include solar irradiance. This study proposes an improved method for short-term prediction of BIPV power generation with simple weather forecast data using feature engineering and machine learning.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
