An Advanced Hybrid Boot-LSTM-ICSO-PP Approach for Day-Ahead Probabilistic PV Power Yield Forecasting and Intra-Hour Power Fluctuation Estimation

Abstract

Probabilistic forecasting models have been developed over the past years in order to aid in the estimation of the uncertainty of the predictive results. A hybrid, bootstrapping long-short term memory (Boot-LSTM)-based model is proposed in this paper, in order to construct accurate prediction intervals (PIs) for short-term solar power generation. A novel approach that introduces an improved chicken swarm optimization (ICSO) algorithm along with a prey-predator (PP) mechanism is developed in order to optimize the predictive accuracy. Exploiting the ICSO’s ability to optimize the position of the swarm’s particles as well as the PP’s ability to further improve the particles’ searching possibilities, the weights and biases of the neurons of the neural network (NN) of the model are optimized and the predictive accuracy is further improved. The accuracy of the PIs is evaluated by minimizing the coverage width criterion (CWC) cost function. The efficiency and the accuracy of the proposed hybrid Boot-LSTM-ICSO-PP model is confirmed via comparing the predictive outputs with state-of-the-art methodologies considering probabilistic evaluation metrics. The proposed model was applied on two datasets of existing solar parks and was further analyzed from a seasonal perspective, in order to prove its efficiency with real-life cases. In terms of CWC minimization, the proposed model, for the first PV park, achieves a 60.3% and 46.94% average improvement compared to the base BELM and LSTM models, respectively, while for the second PV park, achieves a 50.64% and 37.87% average improvement to the respective base models as well.

Keywords

• Solar power forecasting
• long short-term memory
• bootstrap
• prediction intervals
• chicken swarm optimizer
• seasonal analysis