Abstract
This paper proposes a new model initialization approach for solar power prediction interval based on the lower and upper bound estimation (LUBE) structure. The linear regression interval estimation (LRIE) was first used to initialize the prediction interval and the extreme learning machine auto encoder (ELM-AE) is then employed to initialize the input weight matrix of the LUBE. Based on the initialized prediction interval and input weight matrix, the output weight matrix of the LUBE could be obtained, which was close to optimal values. The heuristic algorithm was employed to train the LUBE prediction model due to the invalidation of the traditional training approach. The proposed model initialization approach was compared with the point prediction initialization and random initialization approaches. To validate its performance, four heuristic algorithms, including particle swarm optimization (PSO), simulated annealing (SA), harmony search (HS), and differential evolution (DE), were introduced. Based on the experiment results, the proposed model initialization approach with different heuristic algorithms was better than the point prediction initialization and random initialization approaches. The PSO can obtain the best efficiency and effectiveness of the optimal solution searching in four heuristic algorithms. Besides, the ELM-AE can weaken the over-fitting phenomenon of the training model, which is brought in by the heuristic algorithm, and guarantee the model stable output.
Highlights
With the increasing global energy consumption, renewable energy and its application technologies have received extensive attention and are being studied enthusiastically
The width initialization, point initialization, and random initialization approaches were abbreviated as WI, PI and RI
Renewable energy generation forecasting technology contributes to decreasing the uncertainty
Summary
With the increasing global energy consumption, renewable energy and its application technologies have received extensive attention and are being studied enthusiastically. An accurate forecast is required to guarantee the stability and economy of power systems. The randomness and indeterminacy of natural resources bring great difficulties for solar power predictions. Traditional solar power point prediction provides limited forecast information, which causes risk [1]. Solar power interval prediction offering interval information under a certain confidence level breaks a new pathway to handle forecasting uncertainty. The interval prediction technology aims at predicting a narrow interval, encompassing as many predicted points as possible. The high-quality prediction intervals are of benefit to static safety analysis and risk evaluation in power systems. Solar power interval prediction attracts less attention compared to point prediction. The existing prominent interval prediction methods include the statistical method and data-driven method
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