Integrating AI applications in an energy management system

Bann, J.; Irisarri, G.D.; Mokhtari, Soroush; Kirschen, Daniel S.; Miller, B.N.

doi:10.1109/64.642962

Cited by 7 publications

(10 citation statements)

References 4 publications

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“…With information aggregation of all facilities, smart interface of whole site can be constructed to form an operation decision environment. [96][97][98][99][100][101][102][103][104] That indicates operators of the site can make a decision and improve energy efficiency at the building level by energy efficiency indices and future forecast.…”

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%

“…Smart interface integrates open system (including open‐source program), real‐time data, a relational database, a user interface, numerical applications, supervisory control, and configuration control to establish an operator decision environment. Represented research studies include an open system for the management and control of greenhouses 32 ; design a home of the future 96 ; an information monitoring and diagnostic system to improve building operation 97 ; intelligent management of consumers loads 54 ; smart home energy management system configurations and scheduling strategies 98 ; a theory of planned behavior to understand individual's energy‐saving behavior in workplaces 99 ; an adaptable system for energy management in intelligent buildings 100 ; an operation decision environment development for energy management system 101 ; domestic control system with peak load management and own energy production 102 ; energy and buildings data driven parallel prediction of building energy consumption using generative adversarial networks 103 ; a smart card system in a local operating network to form a distributed energy management and control system for buildings 104 ; mining building performance data for energy‐efficient operation 105 ; the infrastructure as a service, platform as a service and software as a service development and integration to enable cloud energy management 106 ; a cloud system for investigating energy‐saving potentials of buildings 107 ; a cloud‐based AI for remote control of air conditioners and massive set‐point behavioral data collection 108 ; a cloud energy management system for small and medium enterprises 109 …”

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%

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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“…A day ahead pricing scheme is developed by the use of the dynamic pricing scheme in an optimization dual problem [38]. An interface between the AI applications and the EMS for intelligent alarm processing, fault diagnosis and power system restoration are presented for a power system model and three different EMS architecture is developed for a common system [39]. There is a cognitive barrier that is faced by most of the power system operators due the large inrush of data from the different portion of the power system while there is an emergency.…”

Section: From Artificial Intelligence To Automated Intelligencementioning

confidence: 99%

A transition from manual to Intelligent Automated power system operation -A Indicative Review

Doddamani¹,

Kapale²

2019

IJECE

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<p>This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.</p>

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Integrating AI applications in an energy management system

Cited by 7 publications

References 4 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

A transition from manual to Intelligent Automated power system operation -A Indicative Review

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