Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities
This work addresses solar energy management for power plant operators by integrating maintenance factors, though it appears incremental as it builds on existing forecasting methods with new data features.
The paper tackles solar power forecasting by incorporating maintenance activities and observed problems alongside weather data, achieving results on a dataset from a 1MW solar power plant with daily entries from 2012 to 2020.
Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the datasets obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis.