Abstract
An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.
Highlights
In recent years, the growth of distributed generation and residential prosumers [1] has motivated energy companies to develop new ways of commercializing energy
Considering the above restrictions, the main objective of our study is to investigate adaptation methods that can be useful for prosumer agents to have more reliable information
In order to validate our proposal, we report the results of a numerical experiment where we use a neural network as the power generation model, a decision tree for the fixed load, and a linear model for the thermal load
Summary
The growth of distributed generation and residential prosumers [1] has motivated energy companies to develop new ways of commercializing energy. Their main objective is to reduce the cost and improve the power system management. Decentralized optimization processes that enable more participation of final customers expected to help this ambition [2]. These systems’ aim of minimizing the energy cost must account for customers comfort. Controllable loads, such as heating, ventilation, and air conditioning (HVAC), allow the cost reduction based on dynamic tariffs by taking into account preferable temperature set-points [4]
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