Abstract
Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.
Highlights
Solar energy is considered a promising power generation candidate [1] for sustainable development and is playing an increasingly important role in response to climate change [2]because of the heavy carbon emission in the conventional power plant [3]
The proposed long short-term memory (LSTM)-Gaussian process regression (GPR) is compared with individual models in terms of point and interval prediction
The point prediction can be obtained by all the involved models, while the prediction interval can only be achieved using LSTM-GPR and individual GPR
Summary
Because of the heavy carbon emission in the conventional power plant [3]. It is applied in distributed and grid-connected systems [4] to power household appliances, commercial, and industrial equipment [5]. Reliable operation and planning of power grids are strongly affected by the deep penetration of solar energy [6], which demands electricity supply companies to achieve uncertainty prediction of solar power and avoid a potential crisis in operation planning in advance. Obtaining short-term solar power forecasting (SPF) results with highly precise point prediction and reliable interval range is becoming a crucial issue in energy management systems. Unlike the energy generated from power plants, the solar power output cannot be absolutely planned and controlled due to some inherent
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