Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison

Carpenter, Joseph; Woodbury, Keith A.; O’Neill, Zheng

doi:10.1016/j.apenergy.2018.01.043

Cited by 26 publications

(10 citation statements)

References 36 publications

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“…RES integration through distributed architectures, often separately analysed in terms of EIPs and at the community level (urban areas) [115], also needs further investigations. In this context, energy management plays a critical role in overall efficiency improvements as well as in CO2 emission reduction [116,117] Therefore, energy efficiency management in energy-intensive industries [118,119] as well as machine-learning techniques for energy management [120,121] can also be studied in in-depth case studies, associated with RES networks.…”

Section: Conceptual Framework and A Research Agendamentioning

confidence: 99%

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

et al. 2019

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Section: Conceptual Framework and A Research Agendamentioning

confidence: 99%

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

et al. 2019

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“…They also collected related information, including weather. Using two sequence-to-sequence modeling methods (seq2seq) and two-dimensional (2D) convolutional neural network (CNN), the model accuracy CV-RMSE was between 0.1394 and 0.5592 [39]. Although the model's accuracy index was high, the quantity of the training data required was huge compared to the cases researched in this study.…”

Section: Comparison With Existing Researchmentioning

confidence: 76%

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

et al. 2022

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Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

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“…• When 𝛼 → ∞ and 𝛽 = 2, the Gaussian ACF is implied, 1,66 which is also known as squared-exponential ACF 3,4,67 and involved in the double-stable model. 2 The temporal ACF cor T (⋅) in discrete time can be, for instance, one of the following: 19…”

Section: Model Specificationmentioning

confidence: 99%

“…While the information on the whole system is of interest, this is typically based on sensor readings, which are limited in both space and time, so it is necessary to surrogate them based on some suitable statistical method 1 . Variables of interest may include, for instance, room temperature, 2 energy consumption, 3 and air quality 4 . We consider the case of enclosed environments, 2,4,5 as contrasted to other applications that are aimed at larger environments like ecosystems 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Separable spatio‐temporal kriging for fast virtual sensing

Miniato

Bellio

Grassetti

et al. 2022

Appl Stoch Models Bus & Ind

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Environmental monitoring is a task that requires to surrogate system-wide information with limited sensor readings. Under the proximity principle, an environmental monitoring system can be based on the virtual sensing logic and then rely on distance-based prediction methods, foremostly spatio-temporal kriging. The last one is cumbersome with large datasets, but we show that a suitable separability assumption reduces its computational cost to an extent broader than considered typically. Only spatial interpolation needs to be performed in a centralized way, while forecasting can be delegated to each sensor. This simplification is related to the fact that two separate models are involved, one in time and one in the space domain. Any of the two models can be replaced without re-estimating the other under a composite likelihood (CL) approach. Moreover, the use of convenient spatial and temporal models eases up computation, not only in the CL approach, but also in maximum likelihood estimation. We show that this perspective on kriging allows to perform virtual sensing even in the case of tall datasets.

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Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison

Cited by 26 publications

References 36 publications

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Separable spatio‐temporal kriging for fast virtual sensing

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