Knowledge-based systems for energy conservation programs

Lara-Rosano, Felipe; Valverde, Nicolás Kemper

doi:10.1016/s0957-4174(97)00069-9

Cited by 7 publications

(11 citation statements)

References 5 publications

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“…The fourth category is the expert system at whole site/building level. Expert systems encompass CBR, 93 KBS, [90][91][92] and RS. 53,94,95 Such technologies upgrade computer systems from data processing systems to information providers.…”

Section: Ai Control Tools For Building Energy Conservationmentioning

confidence: 99%

“…The following three problems are proposed as the unmet demands of AI studies: Lack of a systematic AI implementation methodology for effective energy saving at three building levels.As illustrated in Section 1, none successfully integrated AI at the equipment, facility, and whole building levels. A systematic methodology is expected to solve the first unmet demand and implements AI at three levels for effective building energy saving. A method is needed to demonstrate the energy‐saving effects of AI to building owners.According to References [2–114], 31.9% of studies specifically presented data for energy savings or energy cost savings as percentages. Among these studies, 30.6% used simulations to illustrate the energy consumption differences before and after implementing AI, whereas 69.4% reported results based on experimental data.…”

Section: Demands Unmet In Ai Studies and Research Gapmentioning

confidence: 99%

“…The forth category is expert system and these encompass case-based reasoning (CBR), knowledge-based systems (KBS), and rough sets (RS). Represented research studies include how to provide the building management system with AI through the use of KBS 90 ; knowledgebased systems for energy conservation programs 91 ; KBS for energy-efficient smart home 92 ; energy prediction tool development for commercial buildings using CBR 93 ; cooling load prediction by the combination of RS and ANN-based data fusion 94 ; home energy management system by NN and RS 53 and rule-based approaches for energy saving in an ambient intelligence environment. 95 The fifth category is smart interface.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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Section: Ai Control Tools For Building Energy Conservationmentioning

confidence: 99%

Section: Demands Unmet In Ai Studies and Research Gapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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“…The control methodologies of AI development can be observed by comparing columns one and two of Table 1, which outline the AI tools and related HVAC systems, respectively. There are four main HVAC system applications for AI tools, including (1) medium to large-scale utilities for commercial buildings [10,13,17,20,22,24,27,29,35,43,44,53,57,63,64,66,71,72,73,76,78,80,82,84,87,91,96,100,105], (2) air conditioners or chillers for residential buildings [11,15,18,19,21,36,37,38,39,42,51,52,60,61,62,65,67,68,69,70,72,75,79,83,86,88,92,94,97,98,99,101,102], (3) air conditioning systems for composite buildings [25,…”

Section: Ai Developments and The Applications For Hvac Systemsmentioning

confidence: 99%

“…The most utilized intelligent control functions are the optimized setting and predictive control functions, as shown in Figure 2. First, the optimized setting function utilizes the KBS [11,12,13,43,67,68,84] or CBR [34,78,105] tools from the database block to determine the set point (SP). The similarity index (SI) is employed during the calculation process, as shown in the following equation:SIi=normalf(|yic−yipMVi|) where yic and yip are the neuro outputs of the variable i for the control and past case, respectively.…”

Section: Theoretical Analysis Of Ai Assisted Hvac Controlmentioning

confidence: 99%

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

Cheng

Lee

2019

Sensors

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In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

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Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems

Piltan

Mehmanchi

Ghaderi

2012

Expert Systems with Applications

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Knowledge-based systems for energy conservation programs

Cited by 7 publications

References 5 publications

Artificial intelligence implementation framework development for building energy saving

Artificial intelligence implementation framework development for building energy saving

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems

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