Multi-type Load Forecasting of IES Based on Load Correlation and Stacked Auto-Encode Extreme Learning Machine

Yao, Wang; Ma, Kerui; Li, Xuxia; Liang, Yan; Hu, Yingying; Jia, Li; Liu, Hongli

doi:10.1109/icpes51309.2020.9349738

Cited by 5 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On this basis, Yan et al [16] optimized the parameter settings of the model with particle swarm optimization (PSO). Li et al [17] and Wang et al [18] predicted load using Stacked auto-encoders (SAEs). In addition to network-specific studies, a double objective operation optimization model that considered an integrated demand response (IDR) mechanism was exploited by Wang et al [19].…”

Section: Multi-energy Load Forecastingmentioning

confidence: 99%

Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

2022

View full text Add to dashboard Cite

Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism for alleviating environmental and economic pressures. As a part of demand-side energy prediction, multi-energy load forecasting is a vital precondition for the planning and operation scheduling of IEM. In order to increase data diversity and improve model generalization while protecting data privacy, this paper proposes a method that uses the CNN-Attention-LSTM model based on federated learning to forecast the multi-energy load of IEMs. CNN-Attention-LSTM is the global model for extracting features. Federated learning (FL) helps IEMs to train a forecasting model in a distributed manner without sharing local data. This paper examines the individual, central, and federated models with four federated learning strategies (FedAvg, FedAdagrad, FedYogi, and FedAdam). Moreover, considering that FL uses communication technology, the impact of false data injection attacks (FDIA) is also investigated. The results show that federated models can achieve an accuracy comparable to the central model while having a higher precision than individual models, and FedAdagrad has the best prediction performance. Furthermore, FedAdagrad can maintain stability when attacked by false data injection.

show abstract