Development of a cooling load prediction model for air-conditioning system control of office buildings

Fan, Chengliang; Liao, Yundan; Ding, Yong

doi:10.1093/ijlct/cty057

Cited by 26 publications

(7 citation statements)

References 25 publications

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“…Its specific meaning is to use n-order differential equations to model x variables. The GM (1,4) model is used in this experiment. That is, the first-order differential equation is used to build a model for 4 variables.…”

Section: Grey Predictionmentioning

confidence: 99%

“…Before entering the experimental data, the data should be normalized first to achieve comparability between data indicators. The normalization processing of neural networks generally has a variable value between [ 1, 0],[ 1,1]   and [1,0] . This is done to weaken the impact of some variables on the model when the value is large.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…With the continuous progress and development of load forecasting methods, there are now many different air conditioning load forecasting methods. In practical applications, each forecasting method cannot meet the current project due to its limitations and applicable conditions [1]. This article intends to analyze and compare several typical air-conditioning load forecasting methods based on actual engineering.…”

Section: Introductionmentioning

confidence: 99%

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Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Wang

2022

Applied Mathematics and Nonlinear Sciences

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This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user's comfort. The article's research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.

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Section: Grey Predictionmentioning

confidence: 99%

Section: Bp Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Wang

2022

Applied Mathematics and Nonlinear Sciences

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“…There are research papers that deal with the problem of predicting energy consumption using various algorithms [3,4,5]. AI solutions to predict building cooling loads in order to reduce AC energy are discussed in [6,7]. An algorithm to control the air flow volume in Air Conditioners is described in [8].…”

Section: Background and Related Workmentioning

confidence: 99%

AC Cooling Time Prediction Using Common Representation Model

Kannan

Roy

2020

IEEE Access

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The data generated by Air Conditioner (AC) consists mainly of sensor and control data. This paper will use the data generated from 53,528 ACs to predict the AC cooling time. The cooling time is the time taken by the AC to cool to a desired temperature. We have observed certain important issues in the data gathered from ACs deployed in dynamic real world environments. Poor prediction accuracies are observed for about 76% of the total ACs due to the lack of data regarding the device behavior, AC settings selection behavior and environmental conditions. During the AC operation, it is observed that the user selects only a small subset of the various combinations of the overall possible settings. Due to unavailability of data, Machine Learning (ML) models cannot be generated for new ACs. This leads to a cold start problem. This paper proposes a common AC prediction model that is generated through data shared from multiple connected ACs. Additionally an Auxiliary Task Learning (ATL) based deep learning model will be used for improving prediction accuracy. The proposed solution provides prediction capabilities for all ACs, compared to 24% of ACs supported by individual prediction models.

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“…Thus reducing the energy consumption in buildings is essential in the effort to reduce GHG emissions. One effective method of reducing the energy consumption of buildings is accurate prediction of the HVAC load profile and performance which allows an effective energy management & control plan to be formulated in advance (Fan et al, 2019). There are two methods for predicting HVAC profile and performance, 1) physical simulation models and 2) Soft computational data-driven models.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the IGSHPA Research Track 2022

2022

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This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan).

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Development of a cooling load prediction model for air-conditioning system control of office buildings

Cited by 26 publications

References 25 publications

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

AC Cooling Time Prediction Using Common Representation Model

Proceedings of the IGSHPA Research Track 2022

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