A data-driven framework for abnormally high building energy demand detection with weather and block morphology at community scale

Lin, Qi; Liu, Ke; Hong, Boyeong; Xu, Xiaodong; Chen, Jiayu; Wang, Wei

doi:10.1016/j.jclepro.2022.131602

Cited by 10 publications

(4 citation statements)

References 51 publications

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“…A small impact Variable [186][187][188][189][190][191][192] HVAC systems A large impact A small impact * Variable [193][194][195][196] Weather factors A large impact A large impact A large impact [197][198][199][200] * Being improved.…”

Section: Building Envelope Parameters a Large Impactmentioning

confidence: 99%

“…In some black-box and grey-box models, weather parameters are based on open-source historical weather data such as OpenWeather, which includes measured weather data and disaggregated weather description information. For example, Lin collected weather data from 2015-2018 to create weather characteristics and then applied SVM and ANN models to find days of extremely high electricity usage in different types of buildings [199]. However, there is growing concern that a single weather data set does not accurately represent sufficient weather information, and it is therefore disadvantageous for predicting energy consumption in buildings [200].…”

Section: Weather Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Building Energy Prediction Models and Related Uncertainties: A Review

Chang

Dong

2022

Buildings

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Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the models. First, this paper introduces three types of prediction methods: white-box models, black-box models, and grey-box models. The principles, strengths, shortcomings, and applications of every model are discussed systematically. Second, this paper analyses prediction model uncertainties in terms of human, building, and weather factors. Finally, the research gaps in predicting building energy consumption are summarised in order to guide the optimisation of building energy prediction methods.

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Section: Building Envelope Parameters a Large Impactmentioning

confidence: 99%

Section: Weather Factorsmentioning

confidence: 99%

Building Energy Prediction Models and Related Uncertainties: A Review

Chang

Dong

2022

Buildings

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“…It is useful for the sustainable development of many urban neighborhoods, and its cost-effective institutional design solves the current deficiencies in promoting sustainable urban development to a certain extent. Literature [12] proposes a framework for detecting abnormally high energy demand in urban buildings with the support of machine learning techniques, which can generate preemptive warnings within a few months of the expected unusually high energy consumption of a target building as a prerequisite for energy policies. Literature [13] explores the potential of the machine learning approach Random Forest (RF) as an alternative model for urban flood prediction.…”

Section: Introductionmentioning

confidence: 99%

Urban community governance and machine learning: practice and prospects for intelligent decision making

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

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“…Jahani et al [19] used the Genetic algorithm-based numerical moment matching (GA-NMM) method to predict the monthly electricity consumption of buildings. Lin et al [20] used RF, SVM, and ANN to predict the monthly electricity consumption of buildings.…”

Section: Introductionmentioning

confidence: 99%

Building Energy Models at Different Time Scales Based on Multi-Output Machine Learning

Tian

Zhang

et al. 2022

Buildings

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Machine learning techniques are widely applied in the field of building energy analysis to provide accurate energy models. The majority of previous studies, however, apply single-output machine learning algorithms to predict building energy use. Single-output models are unable to concurrently predict different time scales or various types of energy use. Therefore, this paper investigates the performance of multi-output energy models at three time scales (daily, monthly, and annual) using the Bayesian adaptive spline surface (BASS) and deep neural network (DNN) algorithms. The results indicate that the multi-output models based on the BASS approach combined with the principal component analysis can simultaneously predict accurate energy use at three time scales. The energy predictions also have the same or similar correlation structure as the energy data from the engineering-based EnergyPlus models. Moreover, the results from the multi-time scale BASS models have consistent accumulative features, which means energy use at a larger time scale equals the summation of energy use at a smaller time scale. The multi-output models at various time scales for building energy prediction developed in this research can be used in uncertainty analysis, sensitivity analysis, and calibration of building energy models.

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A data-driven framework for abnormally high building energy demand detection with weather and block morphology at community scale

Cited by 10 publications

References 51 publications

Building Energy Prediction Models and Related Uncertainties: A Review

Building Energy Prediction Models and Related Uncertainties: A Review

Urban community governance and machine learning: practice and prospects for intelligent decision making

Building Energy Models at Different Time Scales Based on Multi-Output Machine Learning

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