Weather Aware Data Cleaning with Denoising AutoEncoder for Solar Power Generation Estimation

Abstract

This paper proposes a data cleaning technique and a prediction method for diagnosing anomalous data of solar power generation facilities and predicting power generation. Accurate solar power generation forecasting plays an important role in optimizing the grid integration of renewable energy sources. One of the main challenges in solar power generation forecasting is the need to identify the presence of anomalous data, which can be considered as bad data affected by weather conditions. The proposed approach utilizes an AutoEncoder (AE), a type of unsupervised neural network, to perform anomaly identification while considering relevant weather conditions. AEs encode input data into a low-dimensional latent space to effectively filter out anomalous data that appears to be noise and capture underlying patterns that are affected by weather variables. For an effective data refinement process, we utilize a Denoising AutoEncoder (DAE) that performs well even with noisy data, which contributes to improving the quality of the data used for solar power generation forecasting. An experimental evaluation was conducted on a real-world PV power generation dataset to compare the forecasting model performance before and after applying DAE to weather input data. The results show that the DAE-based weather-aware data cleaning technique can mitigate the impact of uncertainty in prediction performance caused by noisy data, making the solar power generation prediction model reliable and universally applicable.