The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach DOI Creative Commons
Sara Golmaryami, Manuel L. Nunes, Paula Ferreira

et al.

Smart Energy, Journal Year: 2024, Volume and Issue: 14, P. 100138 - 100138

Published: March 21, 2024

Achieving a sustainable energy future requires clean, affordable supply and active consumer engagement in the market. This study proposes to evaluate simulate consumers' willingness participate demand-side management programs using an agent-based modelling approach address social learning effect as key factor influencing behaviour. The proposed model simulates households' electricity interactions examining how shift usage is encouraged through environment, while accounting for diversity among consumers. Data from survey conducted Portugal, including questions about influence of recommendations friends or family members on individuals' engage demand response activities, are used test simulation. findings reveal that significantly impacts acceptance, yet extent this varies depending socio-economic characteristics confirms effective capturing dynamics supporting market decision making, providing valuable insights devising consumers strategies.

Language: Английский

Performance evaluation of deep learning architectures for load and temperature forecasting under dataset size constraints and seasonality DOI
Wonjun Choi, Sangwon Lee

Energy and Buildings, Journal Year: 2023, Volume and Issue: 288, P. 113027 - 113027

Published: March 30, 2023

Language: Английский

Citations

16
System modeling for grid-interactive efficient building applications DOI Creative Commons
Yunyang Ye, Cary A. Faulkner, Rong Xu

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 69, P. 106148 - 106148

Published: March 1, 2023

Language: Английский

Citations

15
Flexibility provisions through local energy communities: A review DOI Creative Commons
Pavani Ponnaganti, Rakesh Sinha, Jayakrishnan Radhakrishna Pillai

et al.

Next Energy, Journal Year: 2023, Volume and Issue: 1(2), P. 100022 - 100022

Published: May 22, 2023

The energy communities have the potential to accelerate transition and empower consumers, thereby, promoting collaborative social transformation. local can support power grid by offering a variety of flexibility services through demand response, load shifting storage. However, existing electricity markets, tariffs regulations often hindering effective sustainable solutions. This paper provides comprehensive review about designing provisions for in context emerging flexible markets. Further, it also discusses need arrangements, technical designs, their impact on communities. Based reviewed literature findings from research projects at Department Energy, Aalborg University as part SERENE SUSTENANCE EU Horizon 2020 involving communities, future directions will be highlighted.

Language: Английский

Citations

14
DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets DOI Creative Commons
Ashkan Safari,

Hamed Kheirandish Gharehbagh,

Morteza Nazari‐Heris

et al.

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 11

Published: Sept. 28, 2023

Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations optimize distribution, the other featured values, for participants decentralized marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model predicting prices given traditional market. This includes numerous components, including concept of P2P systems, long-term short-term memory (LSTM) networks, decision trees (DT), Blockchain. utilized dataset with 70,084 data points, which included maximum/minimum capacities, well renewable generation, price communities. The developed obtains significant performance 0.000636% Mean Absolute Percentage Error (MAPE) 0.000975% Root Square (RMSPE). DeepResTrade’s demonstrated by its RMSE 0.016079 MAE 0.009125, indicating capacity to reduce difference between anticipated actual prices. performs admirably describing in, shown considerable R2 score 0.999998. Furthermore, F1/recall scores [1, 1, 1] precision all imply accuracy.

Language: Английский

Citations

14
The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach DOI Creative Commons
Sara Golmaryami, Manuel L. Nunes, Paula Ferreira

et al.

Smart Energy, Journal Year: 2024, Volume and Issue: 14, P. 100138 - 100138

Published: March 21, 2024

Achieving a sustainable energy future requires clean, affordable supply and active consumer engagement in the market. This study proposes to evaluate simulate consumers' willingness participate demand-side management programs using an agent-based modelling approach address social learning effect as key factor influencing behaviour. The proposed model simulates households' electricity interactions examining how shift usage is encouraged through environment, while accounting for diversity among consumers. Data from survey conducted Portugal, including questions about influence of recommendations friends or family members on individuals' engage demand response activities, are used test simulation. findings reveal that significantly impacts acceptance, yet extent this varies depending socio-economic characteristics confirms effective capturing dynamics supporting market decision making, providing valuable insights devising consumers strategies.

Language: Английский

Citations

5