{'fraction_non_alphanumeric': 0.020889487870619946, 'fraction_numerical': 0.0, 'mean_word_length': 5.456521739130435, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A hybrid deep learning model with error correction for photovoltaic power forecasting

The penetration of photovoltaic (PV) power into modern power systems brings enormous economic and environmental benefits due to its cleanness and inexhaustibility. Therefore, accurate PV power forecasting is a pressing and rigid demand to reduce the negative impact of its randomness and intermittency on modern power systems. In this paper, we explore the application of deep learning based hybrid technologies for ultra-short-term PV power forecasting consisting of a feature engineering module, a deep learning-based point prediction module, and an error correction module. The isolated forest based feature preprocessing module is used to detect the outliers in the original data. The non-pooling convolutional neural network (NPCNN), as the deep learning based point prediction module, is developed and trained using the processed data to identify non-linear features. The historical forecasting errors between the forecasting and actual PV data are further constructed and trained to correct the forecasting errors, by using an error correction module based on a hybrid of wavelet transform (WT) and k-nearest neighbor (KNN). In the simulations, the proposed method is extensively evaluated on actual PV data in Limburg, Belgium. Experimental results show that the proposed hybrid model is beneficial for improving the performance of PV power forecasting compared with the benchmark methods.