A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction  

作　　者：Devinder Kaur Shama Naz Islam MdApel Mahmud Md.Enamul Haque Adnan Anwar 

机构地区：[1]School of Engineering,Deakin University,Australia [2]Faculty of Engineering and Environment,Northumbria University,Newcastle Upon Tyne,UK [3]School of Information Technology,Deakin University,Strategic Centre for Cyber Security Research Institute(CSRI),Geelong 3216,Australia

出　　处：《Energy and AI》2023年第4期319-328,共10页能源与人工智能（英文）

摘　　要：The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.

关 键 词：Bayesian deep learning Bidirectional long-short term memory Dimensionality reduction Generation forecasting Renewable power generation Variational auto-encoders 

分 类 号：TN9[电子电信—信息与通信工程]