Hourly energy profile determination technique from monthly energy bills

Lamagna, Mario; Nastasi, Benedetto; Groppi, Daniele; Nezhad, Meysam Majidi; Garcia, Davide Astiaso

doi:10.1007/s12273-020-0698-y

Cited by 23 publications

(10 citation statements)

References 29 publications

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“…The authors discovered how energy consumption is changed during the daytime and consequently, they plotted the typical consumption patterns of four groups of buildings. Those patterns are the basis for modeling higher resolution profiles from monthly bills [29] or to evaluate flexibility potential of the built environment [30].…”

Section: Related Workmentioning

confidence: 99%

A New Generation of Thermal Energy Benchmarks for University Buildings

et al. 2020

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In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m2/yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m2/yr in January to one and nearly zero kWh/m2/yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.

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Section: Related Workmentioning

confidence: 99%

A New Generation of Thermal Energy Benchmarks for University Buildings

et al. 2020

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“…The readers could further explore articles devoted specifically to national frameworks discussing FC adoption strategies to gain regional insights [28–31] . Reviewing opportunities, limitations, and recommendations/suggestions related to specific applications such as building sector, [32] heavy‐duty trucks, [33] maritime application, [34] electric vehicles, [35] and aircraft electrification, [36] etc. of FCs were carried out by the contemporary researchers.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review on Hydrogen Based Fuel Cell Technology and Applications

Gupta,

Toksha,

Rahaman

2023

The Chemical Record

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The research in energy storage and conversion is playing a critical role in energy policy as the innovation and technological progress are essential for achieving the energy transition and climate neutrality goals. Hydrogen Fuel Cell technology is considered a strategic element in the pursuit of sustainable and clean energy solutions. This technology is increasingly gaining attention in recent years as a potential substitute to conventional non‐renewable energy sources. Fuel cell technology can be employed for domestic/commercial use along with powering the transportation sector which currently employs the use of conventional battery systems. However, these systems pose severe limitations with respect to longer charging times and limited distance range. This review article aims at providing a comprehensive methodical overview of hydrogen‐based fuel cell technology along with key concepts, present day scenarios, including overview of the market and industry trends, government policies and initiatives, along with major stakeholders involved in scaling up the technology for mass consumption. The outlook of fuel cells, including their capability to revolutionise the energy sector is discussed. The technological advancements and breakthroughs on the horizon along with the challenges and safety concerns related to the widespread acceptance of fuel cells are analysed.

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“…If monthly electricity and gas usage data (in utility bills) are available for a building, Bayesian analysis (Heo, Choudhary, and Augenbroe 2012), pattern matching (Sun et al 2016), and multi-objective optimization methods can be used to calibrate the building energy model at the individual building level. Mathematical techniques are sometimes used to infer hourly energy profile from monthly utility bills (Lamagna et al 2020). If finer granular data are available (usually limited to a few buildings), e.g., smart meter data with 15minute electricity usage and daily natural gas usage data, calibration methods including Bayesian analysis and multi-objective optimization can be applied.…”

Section: Introductionmentioning

confidence: 99%

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

et al. 2022

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Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

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Hourly energy profile determination technique from monthly energy bills

Cited by 23 publications

References 29 publications

A New Generation of Thermal Energy Benchmarks for University Buildings

A New Generation of Thermal Energy Benchmarks for University Buildings

A Critical Review on Hydrogen Based Fuel Cell Technology and Applications

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

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