Assessment of Model-Based peak electric consumption prediction for commercial buildings

Granderson, Jessica; Sharma, Mrinalini; Crowe, Eliot; Jump, David; Fernandes, Samuel; Touzani, Samir; Johnson, Devan

doi:10.1016/j.enbuild.2021.111031

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…In other words, the measured shed is less than the real shed. Interestingly, this conclusion agrees with existing research studies that state that baseline models tend to understate the achieved load reductions (Coughlin et al, 2009;Granderson et al, 2021). This could result in a systematically lower compensation for the building that provided the services.…”

Section: Event-specific Performancesupporting

confidence: 89%

“…Some works have investigated the uncertainty introduced by the source and frequency of the input data used on the model prediction (Coughlin et al, 2009;Granderson and Price, 2014). Similarly, a more recent work explored the bias of common baseline models when evaluating peak electricity load reduction (Granderson et al, 2021). One of the strengths of these studies is the use of real building experimental results as opposed to simulated.…”

Section: Existing Researchmentioning

confidence: 99%

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Factors contributing to measurement uncertainty of HVAC-enabled demand flexibility in grid-interactive commercial buildings

Vindel¹,

Akinci²,

Bergés³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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The importance of evaluating the performance of electric power demand flexibility in buildings has increased in recent years due to the expected participation of demand resources in reliability-based grid services. All demand flexibility assessments involve a degree of uncertainty when measuring the magnitude of demand response events, due to the uncertainty in the counterfactual load estimation. The objective of this work is to understand how distinct factors contribute to uncertainty in measuring HVAC-enabled demand response in buildings. More specifically, we will vary two factors that are known to contribute to the uncertainty of the observed event: choice of baseline model and choice of assessment boundary. The practical implication of our analysis is to contribute to the design of improved measurement and verification protocols for demand flexibility in buildings.

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Section: Event-specific Performancesupporting

confidence: 89%

Section: Existing Researchmentioning

confidence: 99%

Factors contributing to measurement uncertainty of HVAC-enabled demand flexibility in grid-interactive commercial buildings

Vindel¹,

Akinci²,

Bergés³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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show abstract

“…Forecasting Strategy Time Scale Description Granderson, Sharma [33] Electricity peak load was evaluated using meter-based modeling for commercial buildings while demand response (DR) application has used Hourly This paper examined whether a regression model that used for predicting the hourly energy consumption is also accurate in predicting short-term peak loads. Thus, an examination of the eight different algorithms was conducted, and the findings are included in the report.…”

Section: Referencesmentioning

confidence: 99%

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang¹,

Hasan

Deng³

et al. 2022

Energy

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“…An algorithm for the characterization of building performance data for use in a clustering process was given by Miller et al [121]. Hourly energy load profile can be estimated, according to Granderson et al [122], using the time of the week technique, in which predicted energy consumption is a combination of two terms that relate the energy consumption to the time of the week and the piecewise continuous effect of the temperature. This method has proven effective in demand response analysis in the case of electricity load events, according to Mathieu et al [123].…”

Section: Disscusionmentioning

confidence: 99%

A Method of Multi-Criteria Assessment of the Building Energy Consumption

et al. 2022

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The aim of this study is to develop a universal method for the unequivocal selection of the optimal combination of components determining the energy efficiency of buildings by means of an introduced global building energy consumption indicator. The basis of this method is a multi-criteria optimization of the components influencing the energy efficiency of buildings. The method requires the development of a detailed description of the set of components influencing the energy efficiency of buildings and the definition of the analysis criteria. The following analysis criteria were adopted: relative annual demand for final energy, cost, durability, and investment outlays payback time. The normalized values of these criteria are calculated in relation to the structure of a reference building. The proposed method makes it possible to explicitly indicate the most advantageous solution from the point of view of the weighted share of the individual criteria. The verification of the method was presented on the basis of the thermo modernization case study analysis for the adopted reference building. As a result, the most advantageous variant of modernization was determined, defined by the lowest value of the global building energy consumption indicator. A high effectiveness of the proposed method has been shown in relation to both the selection of the modernization option and the assessment of the individual component share in each modernization solution.

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Assessment of Model-Based peak electric consumption prediction for commercial buildings

Cited by 10 publications

References 12 publications

Factors contributing to measurement uncertainty of HVAC-enabled demand flexibility in grid-interactive commercial buildings

Factors contributing to measurement uncertainty of HVAC-enabled demand flexibility in grid-interactive commercial buildings

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

A Method of Multi-Criteria Assessment of the Building Energy Consumption

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