An Expert System for Building Energy Management Through the Web of Things

Ibaseta, Daniel; Molleda, Julio; Alvarez, M Gándara; Díez, Fidel

doi:10.1007/978-3-030-61705-9_39

Cited by 1 publication

(1 citation statement)

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“…The aim of these rule-based systems is to computationally emulate the decision-making process of a human expert to allow to the target machine perform intelligent actions to solve a fixed problem. Some authors used rule-based systems as the backbone of their systems to perform intelligent control management of different systems [9], [10] or to detect faults or problems in the system life cycle [11]. Likewise, some of these works have been applied to real world buildings [12], [13].…”

Section: A Ai and Expert Rule Systemsmentioning

confidence: 99%

Designing a generalised reward for Building Energy Management Reinforcement Learning agents

Mulero

Arregi

Beitia

et al. 2021

2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)

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The reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.

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Section: A Ai and Expert Rule Systemsmentioning

confidence: 99%