Residential Demand Response Algorithms: State-of-the-Art, Key Issues and Challenges

Author(s):  
Rajasekhar Batchu ◽  
Naran M. Pindoriya
2021 ◽  
Vol 9 (1) ◽  
pp. 36-44
Author(s):  
Robert Mieth ◽  
Samrat Acharya ◽  
Ali Hassan ◽  
Yury Dvorkin

Author(s):  
Xiao Kou ◽  
Yan Du ◽  
Fangxing Li ◽  
Hector Pulgar-Painemal ◽  
Helia Zandi ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2795
Author(s):  
Nikolaos Iliopoulos ◽  
Motoharu Onuki ◽  
Miguel Esteban

Residential demand response empowers the role of electricity consumers by allowing them to change their patterns of consumption, which can help balance the energy grid. Although such type of management is envisaged to play an increasingly important role in the integration of renewables into the grid, the factors that influence household engagement in these initiatives have not been fully explored in Japan. This study examines the influence of interpersonal, intrapersonal, and socio-demographic characteristics of households in Yokohama on their willingness to participate in demand response programs. Time of use, real time pricing, critical peak pricing, and direct load control were considered as potential candidates for adoption. In addition, the authors explored the willingness of households to receive non-electricity related information in their in-home displays and participate in a philanthropy-based peer-to-peer energy platform. Primary data were collected though a questionnaire survey and supplemented by key informant interviews. The findings indicate that household income, ownership of electric vehicles, socio-environmental awareness, perceived sense of comfort, control, and complexity, as well as philanthropic inclinations, all constitute drivers that influence demand flexibility. Finally, policy recommendations that could potentially help introduce residential demand response programs to a wider section of the public are also proposed.


2018 ◽  
Vol 96 ◽  
pp. 411-419 ◽  
Author(s):  
Xing Yan ◽  
Yusuf Ozturk ◽  
Zechun Hu ◽  
Yonghua Song

Author(s):  
Ke Xu ◽  
Yifan Zhang ◽  
Deheng Ye ◽  
Peilin Zhao ◽  
Mingkui Tan

Portfolio selection is an important yet challenging task in AI for FinTech. One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions. The existing methods, however, fall short of capturing: 1) the complicated sequential patterns for asset price series and 2) the price correlations among multiple assets. In this paper, under a deep reinforcement learning paradigm for portfolio selection, we propose a novel Relation-aware Transformer (RAT) to handle these aspects. Specifically, being equipped with our newly developed attention modules, RAT is structurally innovated to capture both sequential patterns and asset correlations for portfolio selection. Based on the extracted sequential features, RAT is able to make profitable portfolio decisions regarding each asset via a newly devised leverage operation. Extensive experiments on real-world crypto-currency and stock datasets verify the state-of-the-art performance of RAT.


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