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Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

https://doi.org/10.3390/en12071201
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What this work says

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

inferredcites →https://doi.org/10.1007/s12273-016-0285-4openalex-snapshot
Computational intelligence techniques for HVAC systems: A review
inferredcites →https://doi.org/10.1016/j.advengsoft.2008.05.003openalex-snapshot
Prediction of building energy consumption by using artificial neural networks
inferredcites →https://doi.org/10.1016/j.apenergy.2009.06.028openalex-snapshot
Artificial neural networks for energy analysis of office buildings with daylighting
inferredcites →https://doi.org/10.1016/j.apenergy.2015.08.126openalex-snapshot
Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data
inferredcites →https://doi.org/10.1016/j.buildenv.2015.04.009openalex-snapshot
Identifying behavioural predictors of small power electricity consumption in office buildings
inferredcites →https://doi.org/10.1016/j.enbuild.2008.06.013openalex-snapshot
Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption
inferredcites →https://doi.org/10.1016/j.enbuild.2013.06.007openalex-snapshot
Energy analysis of a building using artificial neural network: A review
inferredcites →https://doi.org/10.1016/j.enbuild.2017.04.038openalex-snapshot
Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
inferredcites →https://doi.org/10.1016/j.enconman.2008.01.035openalex-snapshot
Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors
inferredcites →https://doi.org/10.1016/j.ijhydene.2016.03.152openalex-snapshot
Estimation of electricity demand in the residential buildings of China based on household survey data
inferredcites →https://doi.org/10.1016/j.neucom.2016.08.004openalex-snapshot
Energy consumption prediction of office buildings based on echo state networks
inferredcites →https://doi.org/10.1016/j.scs.2016.08.001openalex-snapshot
Efficient energy modelling of heterogeneous building portfolios
inferredcites →https://doi.org/10.1016/j.scs.2018.05.050openalex-snapshot
Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.1109/72.105422openalex-snapshot
Sensitivity analysis of multilayer perceptron with differentiable activation functions
inferredcites →https://doi.org/10.1109/72.963772openalex-snapshot
Sensitivity analysis of multilayer perceptron to input and weight perturbations
inferredcites →https://doi.org/10.1109/cdc.2014.7040273openalex-snapshot
Energy scheduling of a smart home under nonlinear pricing
inferredcites →https://doi.org/10.1109/icnn.1997.614194openalex-snapshot
Gauss-Newton approximation to Bayesian learning
inferredcites →https://doi.org/10.1109/ijcnn.1992.287175openalex-snapshot
Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions
inferredcites →https://doi.org/10.1109/tase.2016.2593101openalex-snapshot
A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities
inferredcites →https://doi.org/10.1109/tsmc.2016.2521836openalex-snapshot
A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency
inferredcites →https://doi.org/10.1155/2018/8473547openalex-snapshot
Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV
inferredcites →https://doi.org/10.1162/neco.1992.4.3.415openalex-snapshot
Bayesian Interpolation
inferredcites →https://doi.org/10.1162/neco.1992.4.3.448openalex-snapshot
A Practical Bayesian Framework for Backpropagation Networks
inferredcites →https://doi.org/10.1214/ss/1009213004openalex-snapshot
Sensitivity Anaysis as an Ingredient of Modeling
inferredcites →https://doi.org/10.2991/ameii-15.2015.161openalex-snapshot
Short-term Prediction of Building Energy Consumption Based on GALM Neural Network
inferredcites →https://doi.org/10.3390/en10060814openalex-snapshot
Economic Analysis of a Photovoltaic System: A Resource for Residential Households
inferredcites →https://doi.org/10.3390/en11082055openalex-snapshot
Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills
inferredcites →https://doi.org/10.3390/en6084308openalex-snapshot
A Multi-Criteria Methodology to Support Public Administration Decision Making Concerning Sustainable Energy Action Plans
inferredcites →https://doi.org/10.5860/choice.31-5500openalex-snapshot
Neural networks for pattern recognition

What points at this work

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

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Advanced techniques for electricity consumption prediction in buildings using comparative correlation analysis, data normalization, and Long Short-Term Memory (LSTM) networks: A case study of a U.S. commercial building
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inferred← citeshttps://doi.org/10.1016/j.jclepro.2020.121160openalex-snapshot
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Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy
inferred← citeshttps://doi.org/10.1016/j.scs.2020.102321openalex-snapshot
Impact of correlation of plug load data, occupancy rates and local weather conditions on electricity consumption in a building using four back-propagation neural network models
inferred← citeshttps://doi.org/10.1016/j.scs.2020.102385openalex-snapshot
Predictions of electricity consumption in a campus building using occupant rates and weather elements with sensitivity analysis: Artificial neural network vs. linear regression
inferred← citeshttps://doi.org/10.1016/j.scs.2022.103981openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.scs.2024.105570openalex-snapshot
Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models
inferred← citeshttps://doi.org/10.1051/e3sconf/202567207008openalex-snapshot
Analysing the Impact of Data Normalization on Levenberg-Marquardt Backpropagation (LMBP) Neural Network Predictions for Indoor Air Quality
inferred← citeshttps://doi.org/10.1109/icosec67334.2025.11459797openalex-snapshot
A Hybrid GWO-CatBoost Model for Predicting Electricity Consumption in Commercial and Residential Buildings
inferred← citeshttps://doi.org/10.1109/isocc47750.2019.9027650openalex-snapshot
Data Gathering and Application to Building Energy Optimization with Sensitivity Analysis for IoT Applications
inferred← citeshttps://doi.org/10.17559/tv-20200507022306openalex-snapshot
Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network
inferred← citeshttps://doi.org/10.24846/v32i1y202307openalex-snapshot
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Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)
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Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks
inferred← citeshttps://doi.org/10.3390/pr7100731openalex-snapshot
Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation
inferred← citeshttps://doi.org/10.3390/su16177805openalex-snapshot
A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks
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Monthly Electricity Consumption Prediction: Integrating Artificial Neural Networks and Calculated Attributes
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