A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems

Alcalá, Rafael; Casillas, Jorge; Cordón, Óscar; González, Antonio; Herrera, F.

doi:10.1016/j.engappai.2004.09.007

Cited by 104 publications

(57 citation statements)

References 42 publications

(47 reference statements)

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“…A PMV-based fuzzy controller was chosen by (Dounis & Manolakis, 2001) while (Kolokotsa et al 2001) presented a family of fuzzy controllers that regulate also air quality and visual comfort. In (Alcala et al, 2005) review of artificial intelligence in buildings with a focus on fuzzy systems is assessed by (Kolokotsa, 2006). Genetic algorithms have been used to optimize the parameters of control systems in (Huang & Lam, 1997) as well as in (Kolokotsa et al, 2002).…”

Section: State Of the Artmentioning

confidence: 99%

Reinforcement Learning for Building Environmental Control

Dalamagkidis¹,

Kolokotsa²

2008

Reinforcement Learning

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Section: State Of the Artmentioning

confidence: 99%

Reinforcement Learning for Building Environmental Control

Dalamagkidis¹,

Kolokotsa²

2008

Reinforcement Learning

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“…Methods for HVAC control, such as rule set based intelligent decision have been proposed [5,6]. For the optimal control of specific systems, various fuzzy control and neural networks have been studied [7,8]. Nevertheless, the conventional BEMS is reactive because they are equipped with simple controllers that track the set-points dictated by the operator.…”

Section: Introductionmentioning

confidence: 99%

Group Building Based Power Consumption Scheduling for the Electricity Cost Minimization with Peak Load Reduction

Oh¹,

Park²,

Son³

2014

Journal of Electrical Engineering and Technology

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-In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

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“…the number of variables to be controlled. Thus, one may use a supervised neural network approach [4], or a supervised auto-tuning of parameters with two-variable fuzzy control [2], or a genetic algorithm for dynamic tuning of rule weighing of an expert system [5]. However, such supervised learning approaches depend on the expertise" knowledge thus it can be environment sensitive or the quality of the expertise.…”

Section: Introductionmentioning

confidence: 99%