Application of automated measurement and verification to utility energy efficiency program data

Granderson, Jessica; Touzani, Samir; Fernandes, Samuel; Taylor, Cody

doi:10.1016/j.enbuild.2017.02.040

Cited by 45 publications

(25 citation statements)

References 5 publications

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“…Ke et al created a cloud-based platform for estimating energy savings for any ECM with a case study in a commercial building [20]. Granderson et al applied an automated whole-building M&V tool to historic data sets from energy efficiency programs and performed a comparison with traditional methods [12]. Ke et al also applied eQUEST software to calibrate energy simulation results using IPMVP Option D [21].…”

Section: Methodologiesmentioning

confidence: 99%

“…In recent years, M&V 2.0 represents an area of significant interest and it is being used to further develop the commonly used practices. M&V 2.0 differs from traditional M&V as it uses large data sets and automated advanced analytics to streamline and scale the process [12]. The automated analytics can provide ongoing savings estimates in close to real-time.…”

Section: Introductionmentioning

confidence: 99%

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Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Gallagher

Leahy

O’Donovan

et al. 2018

Energy and Buildings

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The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM's). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a particular focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate and reliable quantification of savings.A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology's ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances.

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Section: Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Gallagher

Leahy

O’Donovan

et al. 2018

Energy and Buildings

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show abstract

“…Measurement and verification (M&V) is one of the critical processes used to evaluate the effectiveness of ECMs. The term "M&V 2.0" is being used in recent years to describe the automated and streamlined approach that uses large datasets and computational automation for the M&V process, where machine learning models are playing an increasingly important role [142]. Colm et al [143] develop a platform using machine learning methods to enable automated, accurate, and reliable quantifications of energy savings in the M&V process.…”

Section: Evaluate Energy Conservation Measuresmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

234

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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“…The next study used 537 buildings and applied ten prediction models to understand further which model performed best among a large set of building [14]. The final study focused on the evaluation of what percentage of the building stock are appropriate to be used with automated methods as developed in the previous two studies [15].…”

Section: Contemporary Building Energy Predictionmentioning

confidence: 99%

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

Miller

2019

MAKE

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Prediction is a common machine learning (ML) technique used on building energy consumption data. This process is valuable for anomaly detection, load profile-based building control and measurement and verification procedures. Hundreds of building energy prediction techniques have been developed over the last three decades, yet there is still no consensus on which techniques are the most effective for various building types. In addition, many of the techniques developed are not publicly available to the general research community. This paper outlines a library of open-source regression techniques from the Scikit-Learn Python library and describes the process of applying them to open hourly electrical meter data from 482 non-residential buildings from the Building Data Genome Project. The results illustrate that there are several techniques, notably decision tree-based models, that perform well on two-thirds of the total cohort of buildings. However, over one-third of the buildings, specifically primary schools, performed poorly. This example implementation shows that there is no one size-fits-all modeling solution and that various types of temporal behavior are difficult to capture using machine learning. An analysis of the generalizability of the models tested motivates the need for the application of future techniques to a board range of building types and behaviors. The importance of this type of scalability analysis is discussed in the context of the growth of energy meter and other Internet-of-Things (IoT) data streams in the built environment. This framework is designed to be an example baseline implementation for other building energy data prediction methods as applied to a larger population of buildings. For reproducibility, the entire code base and data sets are found on Github.

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Application of automated measurement and verification to utility energy efficiency program data

Cited by 45 publications

References 5 publications

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

State-of-the-art on research and applications of machine learning in the building life cycle

More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data

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