In the quest to reduce carbon emissions and transition to cleaner energy sources, solar photovoltaic (Solar-PV) power generation has emerged as a promising solution. However, the intermittent nature of solar power poses significant challenges for grid management and energy planning. Accurate forecasting of solar power generation is crucial for ensuring grid stability and optimizing resource allocation. A recent study published in the journal “IEEE Access” titled “A Compound of Deep-Learning and Feature Selection for Solar Power Forecasting Applications” presents a novel approach to enhance the precision of solar power forecasting, potentially revolutionizing the energy sector.
The research, led by Praveen Kumar Singh from the Department of Computer Applications at Manipal University Jaipur, India, introduces an improved hybrid deep-learning model named the improved WT-LSTM model. This innovative method combines feature selection techniques with a wavelet transform-based decomposition of historical solar power generation data. “The key idea is to identify the most relevant features that impact solar power generation and use them to improve the forecasting accuracy,” explains Singh.
The study employs various feature selection methods, including LASSO regression, coefficient of determination, forward selection, backward selection, and mutual information, to identify the most significant features. These features are then used in the improved WT-LSTM model, which decomposes the historical data into various frequency components and extracts statistical features. These features, combined with meteorological data, form the basis for solar power generation forecasting.
The improved WT-LSTM model incorporates batch normalization, dropout, and L2 regularization to enhance its forecasting capabilities for various time horizons. The model’s performance was compared with other deep-learning models, including Feedforward Neural Network (FFNN), 1D Convolutional Neural Network (1D CNN), Bidirectional Long Short-Term Memory (Bi LSTM), and Long Short-Term Memory (LSTM), as well as a basic hybrid WT-LSTM model. The datasets used for testing were from two distinct sites: 1A DKASC and 1B DKASC Alice Springs Solar-PV system, covering the period from January 1, 2019, to December 31, 2019, with a 5-minute resolution.
The results were impressive. For a 15-minute forecasting horizon, the improved WT-LSTM model achieved a Mean Absolute Error (MAE) of 0.38617 and 0.29736, a Root Mean Square Error (RMSE) of 0.78038 and 0.64868, and R2 values of 0.95423 and 0.95592 across the two datasets. The model also performed well for 30-minute and 60-minute forecasting horizons, indicating its superior forecasting accuracy.
The implications of this research are significant for the energy sector. Accurate solar power forecasting can enhance grid stability, optimize resource allocation, and reduce the need for backup power generation. “This model can be a game-changer for renewable-dominated power systems, enabling better generation planning and reserve estimation,” says Singh.
The study’s findings suggest that the improved WT-LSTM model could play a pivotal role in the transition to cleaner energy sources. As the world continues to grapple with the challenges of climate change, innovative solutions like this one are crucial for building a sustainable future.
Published in the peer-reviewed journal “IEEE Access,” which translates to “Institute of Electrical and Electronics Engineers Access,” the research underscores the potential of deep learning in transforming the energy sector. By improving the accuracy of solar power forecasting, this model could help mitigate the impact of conventional fuels and pave the way for a more sustainable energy landscape.