Short-term prediction of solar irradiance based on TCB-GRU-MLP
Open Access
- 5 July 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 11-17
Abstract
In recent years, with the continuous growth of global new energy installed capacity, it is imperative to improve the prediction accuracy of new energy power generation. Based upon the current development trends of photovoltaic power generation and deep learning, this paper proposes a solar irradiance prediction model based on TCB-GRU-MLP. First, based on the Pearson and Spearman correlation analysis methods, the influencing factors that are strongly related to photovoltaic meteorological factors and irradiance in the study area were selected; then, the solar irradiance is predicted, and the results are displayed in the visualization interface. Experimental results show that the trained prediction model performs better than the TCN and Transformer models.Keywords
This publication has 13 references indexed in Scilit:
- An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extractionRenewable Energy, 2023
- A CMPA based cost-effective photovoltaic power generation system and utilizationMicrosystem Technologies, 2023
- Deep learning algorithms for very short term solar irradiance forecasting: A surveyRenewable and Sustainable Energy Reviews, 2023
- Comparison between GRU and BP Neural Networks for Short-Term Prediction of Solar Irradiance2023
- Intelligent Solar Irradiance Forecasting Using Hybrid Deep Learning Model: A Meta-Heuristic-Based PredictionNeural Processing Letters, 2022
- Day-Ahead Nonparametric Probabilistic Forecasting of Photovoltaic Power Generation Based on the LSTM-QRA Ensemble ModelIEEE Access, 2020
- A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid modelsInternational Journal of Energy Research, 2020
- A Deep Physical Model for Solar Irradiance Forecasting with Fisheye Images2020
- Mitigating methods of power fluctuation of photovoltaic (PV) sources – A reviewRenewable and Sustainable Energy Reviews, 2016
- Neural Network Based on Dynamic Multi-swarm Particle Swarm Optimizer for Ultra-Short-Term Load Forecasting2014