Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level

Glasgo, Brock; Hendrickson, Chris; Azevedo, Inês L.

doi:10.1016/j.apenergy.2017.05.164

Cited by 27 publications

(9 citation statements)

References 15 publications

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“…Treating raw data according to only the market and technologies layers, without considering end-users and stakeholders, is already inducing unneglectable simulation uncertainties on an hourly basis, and the high prediction errors may induce significant organizational and structural errors. The data purged of bias will help us to focus on specific smart energy products and services, their research and development [68]. Pecan Street's recent data analyses from 12,083 monitored residences showed that 50% of the electricity used was related to air conditioning units, water heaters, and refrigerators [69].…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

et al. 2018

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In this paper, a framework is presented for the evaluation of smart grid environment which is called the three-layer model. This three-layer model comprises three specific categories, or ‘layers’, namely, the stakeholder, market and technologies layers. Each layer is defined and explored herein, using an extensive literature study regarding their key elements, their descriptions and an overview of the findings from the literature. The assumption behind this study is that a solid understanding of each of the three layers and their interrelations will help in more effective assessment of residential smart grid pilots in order to better design products and services and deploy smart grid technologies in networks. Based on our review, we conclude that, in many studies, social factors associated with smart grid pilots, such as markets, social acceptance, and end-user and stakeholder demands, are most commonly defined as uncertainties and are therefore considered separately from the technical aspects of smart grids. As such, it is recommended that, in future assessments, the stakeholder and market layers should be combined with the technologies layer so as to enhance interaction between these three layers, and to be able to better evaluate residential smart energy systems in a multidisciplinary context.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

et al. 2018

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“…Only through a detailed analysis of the trends in the use of energy, and the use of a more systematic analysis model to analyze the data and information of current energy use, and to predict and analyze the sustainability, use time, and effectiveness of subsequent energy sources, can we eventually become China. The long-term use of energy provides data and theoretical support [14]. However, in the traditional energy use analysis process in China, many scholars simply use a simple data analysis model to predict energy use.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Guangdong Energy Demand Forecast Based on DIP-REM Model

Zhang

2021

E3S Web Conf.

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Computer data mining technology has a positive impact on the accurate prediction of China's energy demand. In order to effectively alleviate the energy crisis that China is currently facing, and further forecast the energy demand, it will provide an active driving force for the sustainable economic development of our country in the future. In this study, the authors studied the current energy use trends in China, and then the authors used the DIP-REM model to predict their future development needs based on a study of the use of energy resources in Guangdong Province. This model can accurately predict the future energy expansion value of Guangdong Province. The research aims to provide a reference for related research on China's energy demand forecast.

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“…Accuracy is the measure of how many correct forecasts are made. Hong et al 49 and Glasgo et al 50 referred to different standards such as Federal Energy Management Program, theInternational Performance Measurement and Verification Protocol and American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) used worldwide. Glasgo et al used ASHRAE Guideline 14 for the CV(RMSE) and relative annual error as reference value for comparison of energy plus simulation and actual Pecan Street home data collected using energy audits and homeowner survey records.…”

Section: Model Validationmentioning

confidence: 99%

A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile

et al. 2020

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Analyzing demand behavior of end consumers is pivotal in long term energy planning. Various models exist for simulating household load profiles to cater different purposes. A macroscopic viewpoint necessitates modeling of a large-scale population at an aggregate level, whereas a microscopic perspective requires measuring loads at a granular level, pertinent to the individual devices of a household. Both aspects have lucrative benefits, instigating the need to combine them into a modeling framework which allows model scalability and flexibility, and to analyze domestic electricity consumption at different resolutions. In this applied research, we propose a multi-resolution agent-based modeling and simulation (ABMS) framework for estimating domestic electricity consumption. Our proposed framework simulates per minute electricity consumption by combining large neighborhoods, the behavior of household individuals, their interactions with the electrical appliances, their sociological habits and the effects of exogenous conditions such as weather and seasons. In comparison with the existing energy models, our framework uniquely provides a hierarchical, multi-scale, multi-resolution implementation using a multi-layer architecture. This allows the modelers flexibility in order to model large-scale neighborhoods at one end, without any loss of expressiveness in modeling microscopic details of individuals’ activities at house level, and energy consumption at the appliance level, at the other end. The validity of our framework is demonstrated using a case study of 264 houses. A validated ABMS framework will support: (a) Effective energy planning; (b) Estimation of the future energy demand; (c) and the analysis of the complex dynamic behavior of the consumers.

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Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level

Cited by 27 publications

References 15 publications

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

An Exploration of the Three-Layer Model Including Stakeholders, Markets and Technologies for Assessments of Residential Smart Grids

Analysis of Guangdong Energy Demand Forecast Based on DIP-REM Model

A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile

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