Short-Term Solar Power Prediction using Machine Learning Algorithms : A High Performing approach
DOI:
https://doi.org/10.3329/gubjse.v9i1.74890Keywords:
LSTM, ANN, SVM, principal component analysis, PV Energy, renewable energyAbstract
The use of solar energy is growing in popularity across the globe as a clean and sustainable energy source. Nevertheless, integrating solar power into the grid and guaranteeing a steady supply of electricity is made more difficult by the weather patterns’ tendency to cause unpredictability in solar power generation. One potential solution to these issues is the use of machine learning (ML) techniques for short term solar photovoltaic (PV) power forecasting. This research looks into
how well various machine learning algorithms work for short-term PV power forecasting. It also offers a summary of the variables that affect time-series data performance and suggests high-performance methods. Long Short-term Memory (LSTM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were tested on an 88,494-item dataset with 20 features at a 15-minute resolution. As accuracy metrics, root mean square error (RMSE) and mean square error (MSE) show that ANNs and LSTM perform better than SVM. The superiority of the suggested methods is demonstrated by a careful comparison with recently published results. The reason for the effectiveness of ANNs and LSTMs is their capacity to represent the complex and non-linear relationships found in the data. Short-term solar power forecasting is complex, and SVMs are better equipped to handle linear relationships. These findings will be a useful guidance for those who intend to work in the field of PV power forecasting, even though further research is required to generalize this observation.
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