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Model input selection for building heating load prediction: A case study for an office building in Tianjin

https://doi.org/10.1016/j.enbuild.2017.11.002
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique
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Applying support vector machine to predict hourly cooling load in the building
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A short-term building cooling load prediction method using deep learning algorithms
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Influence of occupancy-oriented interior cooling load on building cooling load design
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A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain)
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Demonstration of the new ESP-r and TRNSYS co-simulator for modelling solar buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2004.09.009openalex-snapshot
Applying support vector machines to predict building energy consumption in tropical region
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A hybrid decision support system for sustainable office building renovation and energy performance improvement
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Methodology to estimate building energy consumption using EnergyPlus Benchmark Models
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Building hourly thermal load prediction using an indexed ARX model
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Re-evaluation of building cooling load prediction models for use in humid subtropical area
inferredcites →https://doi.org/10.1016/j.enbuild.2014.09.026openalex-snapshot
Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation
inferredcites →https://doi.org/10.1016/j.enbuild.2015.06.039openalex-snapshot
Accuracy analysis of longwave sky radiation models in the MZELWE module of the ESP-r program
inferredcites →https://doi.org/10.1016/j.enbuild.2015.08.041openalex-snapshot
An improved office building cooling load prediction model based on multivariable linear regression
inferredcites →https://doi.org/10.1016/j.enbuild.2015.10.013openalex-snapshot
A new validated TRNSYS module for simulating latent heat storage walls
inferredcites →https://doi.org/10.1016/j.enbuild.2016.02.045openalex-snapshot
Experimental validation of a EnergyPlus model: Application of a multi-storey naturally ventilated double skin façade
inferredcites →https://doi.org/10.1016/j.enbuild.2016.04.067openalex-snapshot
Application of neural networks for evaluating energy performance certificates of residential buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2016.12.016openalex-snapshot
Input variable selection for thermal load predictive models of commercial buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2017.02.064openalex-snapshot
Inverse blackbox modeling of the heating and cooling load in office buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2017.03.042openalex-snapshot
A study on energy performance of 30 commercial office buildings in Hong Kong
inferredcites →https://doi.org/10.1016/j.enbuild.2017.08.077openalex-snapshot
Research on short-term and ultra-short-term cooling load prediction models for office buildings
inferredcites →https://doi.org/10.1016/j.enconman.2003.10.009openalex-snapshot
Cooling load prediction for buildings using general regression neural networks
inferredcites →https://doi.org/10.1016/j.enconman.2010.06.004openalex-snapshot
Multiple regression models for energy use in air-conditioned office buildings in different climates
inferredcites →https://doi.org/10.1016/j.enconman.2011.02.002openalex-snapshot
A study of the importance of occupancy to building cooling load in prediction by intelligent approach
inferredcites →https://doi.org/10.1016/j.enconman.2013.01.002openalex-snapshot
Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong
inferredcites →https://doi.org/10.1016/j.enconman.2013.12.060openalex-snapshot
Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression
inferredcites →https://doi.org/10.1016/j.energy.2015.04.109openalex-snapshot
Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm
inferredcites →https://doi.org/10.1016/j.energy.2016.03.057openalex-snapshot
Simplified dynamic neural network model to predict heating load of a building using Taguchi method
inferredcites →https://doi.org/10.1016/j.ijepes.2014.12.087openalex-snapshot
A new method for short-term load forecasting based on fractal interpretation and wavelet analysis
inferredcites →https://doi.org/10.1016/j.jclepro.2016.07.049openalex-snapshot
ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building
inferredcites →https://doi.org/10.1016/j.jobe.2016.05.007openalex-snapshot
Weather datasets generated using kriging techniques to calibrate building thermal simulations with TRNSYS
inferredcites →https://doi.org/10.1016/j.neucom.2015.03.085openalex-snapshot
A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system
inferredcites →https://doi.org/10.1016/j.neucom.2015.12.004openalex-snapshot
Forecasting electricity load with advanced wavelet neural networks
inferredcites →https://doi.org/10.1016/j.proeng.2015.09.130openalex-snapshot
Impact of Civil Envelope on Energy Consumption based on EnergyPlus
inferredcites →https://doi.org/10.1016/j.renene.2015.05.008openalex-snapshot
Heating and cooling performance characterisation of ground source heat pump system by testing and TRNSYS simulation
inferredcites →https://doi.org/10.1016/j.rser.2015.04.065openalex-snapshot
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
inferredcites →https://doi.org/10.1016/j.rser.2017.09.108openalex-snapshot
A review of data-driven approaches for prediction and classification of building energy consumption
inferredcites →https://doi.org/10.1080/00207160802033582openalex-snapshot
Vapnik's learning theory applied to energy consumption forecasts in residential buildings
inferredcites →https://doi.org/10.1142/s0217979208047444openalex-snapshot
INITIAL SHIP DESIGN USING A PEARSON CORRELATION COEFFICIENT AND ARTIFICIAL INTELLIGENCE TECHNIQUES
inferredcites →https://doi.org/10.1177/2041296710394287openalex-snapshot
Real-time combined heat and power operational strategy using a hierarchical optimization algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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ASSESSMENT OF THE INFLUENCE OF VACUUM REPLACEMENT ON ENERGY CONSUMPTION AND CONDITIONS OF COMFORT IN BUILDING ON THE BASIS OF DYNAMIC MODELING
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A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions
inferred← citeshttps://doi.org/10.3390/buildings13020312openalex-snapshot
Machine Learning Approach to Predict Building Thermal Load Considering Feature Variable Dimensions: An Office Building Case Study
inferred← citeshttps://doi.org/10.3390/buildings13020532openalex-snapshot
A Review of Data-Driven Building Energy Prediction
inferred← citeshttps://doi.org/10.3390/buildings13061423openalex-snapshot
Improved Data-Driven Building Daily Energy Consumption Prediction Models Based on Balance Point Temperature
inferred← citeshttps://doi.org/10.3390/buildings13092340openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/buildings15071008openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en11020395openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en13226079openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/en14227512openalex-snapshot
Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
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Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones
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inferred← citeshttps://doi.org/10.3390/su15042884openalex-snapshot
Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance
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Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network
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Use of Smart Technology for heating energy optimization in buildings : experimental and numerical developments for indoor temperature forecasting
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