On the public citation graph

Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms

https://doi.org/10.3390/su13020744

0 signed · 72 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1016/j.apenergy.2019.03.078openalex-snapshot

Vector field-based support vector regression for building energy consumption prediction

inferredcites →https://doi.org/10.1016/j.apenergy.2019.114131openalex-snapshot

A hybrid model for building energy consumption forecasting using long short term memory networks

inferredcites →https://doi.org/10.1016/j.conengprac.2020.104312openalex-snapshot

A semi-physical static model for optimizing power consumption of HVAC systems

inferredcites →https://doi.org/10.1016/j.enbuild.2017.04.032openalex-snapshot

Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks

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.enbuild.2019.109639openalex-snapshot

Predictive chiller operation: A data-driven loading and scheduling approach

inferredcites →https://doi.org/10.1016/j.enbuild.2020.109952openalex-snapshot

A long short-term memory artificial neural network to predict daily HVAC consumption in buildings

inferredcites →https://doi.org/10.1016/j.enconman.2019.111799openalex-snapshot

A review of deep learning for renewable energy forecasting

inferredcites →https://doi.org/10.1016/j.energy.2016.11.064openalex-snapshot

Cascade-based short-term forecasting method of the electric demand of HVAC system

inferredcites →https://doi.org/10.1016/j.energy.2018.01.177openalex-snapshot

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

inferredcites →https://doi.org/10.1016/j.energy.2019.05.230openalex-snapshot

Predicting residential energy consumption using CNN-LSTM neural networks

inferredcites →https://doi.org/10.1016/j.energy.2019.116778openalex-snapshot

Effective energy consumption forecasting using enhanced bagged echo state network

inferredcites →https://doi.org/10.1016/j.energy.2020.117197openalex-snapshot

LSTM based long-term energy consumption prediction with periodicity

inferredcites →https://doi.org/10.1016/j.energy.2020.117858openalex-snapshot

A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series

inferredcites →https://doi.org/10.1016/j.epsr.2019.106073openalex-snapshot

Load demand forecasting of residential buildings using a deep learning model

inferredcites →https://doi.org/10.1016/j.ijrefrig.2019.07.018openalex-snapshot

A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction

inferredcites →https://doi.org/10.1016/j.jobe.2019.101044openalex-snapshot

Optimal chiller loading in HVAC System Using a Novel Algorithm Based on the distributed framework

inferredcites →https://doi.org/10.1016/j.measurement.2019.01.008openalex-snapshot

Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods

inferredcites →https://doi.org/10.1016/j.patrec.2020.07.042openalex-snapshot

Comparative analysis of image classification algorithms based on traditional machine learning and deep learning

inferredcites →https://doi.org/10.1016/j.scs.2017.01.019openalex-snapshot

Quantitative analysis of the impact of maintenance management on the energy consumption of a hospital in Extremadura (Spain)

inferredcites →https://doi.org/10.1016/j.scs.2019.101698openalex-snapshot

A simplified HVAC energy prediction method based on degree-day

inferredcites →https://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

inferredcites →https://doi.org/10.1016/j.seta.2019.06.002openalex-snapshot

Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

inferredcites →https://doi.org/10.1109/isie.2017.8001465openalex-snapshot

Deep neural networks for energy load forecasting

inferredcites →https://doi.org/10.18280/ijht.330116openalex-snapshot

THE LOCAL MEDIA RADIANT TEMPERATURE FOR THE CALCULATION OF COMFORT IN AREAS CHARACTERIZED BY RADIANT SURFACES

inferredcites →https://doi.org/10.18280/ijht.330213openalex-snapshot

SOME OBSERVATIONS ON THE RADIATIVE EXCHANGES INFLUENCE ON THERMAL COMFORT IN RECTANGULAR OPEN-SPACE ENVIRONMENTS

inferredcites →https://doi.org/10.18280/ijht.35sp0112openalex-snapshot

Islands "Smart Energy" for eco-sustainable energy a case study "Favignana Island"

inferredcites →https://doi.org/10.3390/su11051490openalex-snapshot

Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing

inferredcites →https://doi.org/10.3390/su12010109openalex-snapshot

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

inferredcites →https://doi.org/10.3390/su12083177openalex-snapshot

Minutely Active Power Forecasting Models Using Neural Networks

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4646385openalex-extracted

Anessim: A Learning-Based Simulation Platform for Closed-Loop Anesthesia Delivery

inferred← citeshttps://doi.org/10.1007/s00521-025-11065-0openalex-snapshot

Time series analysis in compressor-based machines: a survey

inferred← citeshttps://doi.org/10.1007/s12273-025-1293-zopenalex-snapshot

Embedding physical neurons in physics-informed neural networks (EP-PINNs) for enhancing chiller performance prediction

inferred← citeshttps://doi.org/10.1007/s42979-023-02246-6openalex-snapshot

Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models

inferred← citeshttps://doi.org/10.1007/s44327-026-00338-5openalex-snapshot

The association pathways between rapid urban growth and urban governance practices in Hawassa city, Ethiopia

inferred← citeshttps://doi.org/10.1016/j.aei.2023.101978openalex-snapshot

Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data

inferred← citeshttps://doi.org/10.1016/j.apenergy.2023.122190openalex-snapshot

Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network

inferred← citeshttps://doi.org/10.1016/j.apenergy.2025.127335openalex-snapshot

A method for chiller performance modeling via SKR-based neural network under physical constraints

inferred← citeshttps://doi.org/10.1016/j.applthermaleng.2024.124953openalex-snapshot

A thermofluid temperature modeling, prediction of closed-loop cooling system of roll grinder based on circulation differential equations

inferred← citeshttps://doi.org/10.1016/j.applthermaleng.2025.125799openalex-snapshot

Comparative analysis of machine learning models for prediction and forecasting of electric water boilers energy consumption

inferred← citeshttps://doi.org/10.1016/j.buildenv.2024.111553openalex-snapshot

Machine learning-based modeling and fogging prevention strategies for ice rink environments

inferred← citeshttps://doi.org/10.1016/j.csite.2025.107309openalex-snapshot

Hybrid TRRM–deep learning approach for water-cooled centrifugal chiller COP prediction

inferred← citeshttps://doi.org/10.1016/j.enbuild.2025.115881openalex-snapshot

A review of the state-of-the-art data-driven modeling of building HVAC systems

inferred← citeshttps://doi.org/10.1016/j.energy.2024.130925openalex-snapshot

A global optimization method for data center air conditioning water systems based on predictive optimization control

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110472openalex-snapshot

Investigate dual chiller characteristics and improve the performance by integrating machine learning and genetic algorithms

inferred← citeshttps://doi.org/10.1016/j.est.2026.121170openalex-snapshot

IoT-based real-time anomaly detection and power prediction model in ice-storage air-conditioning system

inferred← citeshttps://doi.org/10.1016/j.ijrefrig.2024.07.019openalex-snapshot

Forecasting operation of a chiller plant facility using data-driven models

inferred← citeshttps://doi.org/10.1016/j.jclepro.2024.141259openalex-snapshot

Multi-view Stacked CNN-BiLSTM (MvS CNN-BiLSTM) for urban PM2.5 concentration prediction of India’s polluted cities

inferred← citeshttps://doi.org/10.1016/j.jobe.2024.110411openalex-snapshot

Leveraging multi-zone building data with machine learning-based models and genetic algorithms to optimize air handling units

inferred← citeshttps://doi.org/10.1016/j.jobe.2026.117228openalex-snapshot

Operational-regime decomposition and interpretable predictive modeling for chiller energy use in HVAC: A two-phase data-driven approach

inferred← citeshttps://doi.org/10.1016/j.psep.2023.03.052openalex-snapshot

A novel deep learning model integrating CNN and GRU to predict particulate matter concentrations

inferred← citeshttps://doi.org/10.1016/j.tbench.2025.100200openalex-snapshot

Advanced Deep Learning Models for Improving Movie Rating Predictions: A Benchmarking Study

inferred← citeshttps://doi.org/10.1109/access.2023.3265083openalex-snapshot

Study of Long-Term Energy Storage System Capacity Configuration Based on Improved Grey Forecasting Model

inferred← citeshttps://doi.org/10.1109/access.2024.3388489openalex-snapshot

LCNN: Lightweight CNN Architecture for Software Defect Feature Identification Using Explainable AI

inferred← citeshttps://doi.org/10.1109/ctisc54888.2022.9849774openalex-snapshot

DNS-Tunnet: A Hybrid Approach for DNS Tunneling Detection

inferred← citeshttps://doi.org/10.1109/icaicta67604.2025.11335122openalex-snapshot

Prediction of MMP-9 Inhibitors as Anticancer Therapeutics by Using 1D-CNN Optimized by Monarch Butterfly Algorithm

inferred← citeshttps://doi.org/10.1109/icnsc55942.2022.10004057openalex-snapshot

Energy Efficiency Forecasting for Central Air-conditioning Refrigeration Systems Based on Deep Neural Network

inferred← citeshttps://doi.org/10.1117/12.3047302openalex-snapshot

Digital twin modeling and machine learning frameworks for forecasting multiple microwave ablation volumes

inferred← citeshttps://doi.org/10.26555/jiteki.v9i4.28113openalex-snapshot

Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods

inferred← citeshttps://doi.org/10.29354/diag/183633openalex-snapshot

Novel diabetes classification approach based on CNN-LSTM: Enhanced performance and accuracy

inferred← citeshttps://doi.org/10.3390/buildings12010013openalex-snapshot

Comparative Evaluation of Predicting Energy Consumption of Absorption Heat Pump with Multilayer Shallow Neural Network Training Algorithms

inferred← citeshttps://doi.org/10.3390/buildings13020532openalex-snapshot

A Review of Data-Driven Building Energy Prediction

inferred← citeshttps://doi.org/10.3390/cleantechnol5010007openalex-snapshot

Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review

inferred← citeshttps://doi.org/10.3390/en14206494openalex-snapshot

Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining

inferred← citeshttps://doi.org/10.3390/en18143672openalex-snapshot

Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models

inferred← citeshttps://doi.org/10.3390/en18236363openalex-snapshot

Chiller System Power Prediction by Physical-Informed Neural Network

inferred← citeshttps://doi.org/10.3390/machines12070471openalex-snapshot

Reinforcement Learning-Based Auto-Optimized Parallel Prediction for Air Conditioning Energy Consumption

inferred← citeshttps://doi.org/10.3390/pr11010122openalex-snapshot

Combining Deep Neural Network with Genetic Algorithm for Axial Flow Fan Design and Development

inferred← citeshttps://doi.org/10.3390/s22239517openalex-snapshot

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

inferred← citeshttps://doi.org/10.3390/su17052196openalex-snapshot

Performance Prediction of a Water-Cooled Centrifugal Chiller in Standard Temperature Conditions Using In-Situ Measurement Data

inferred← citeshttps://doi.org/10.3390/technologies12040043openalex-snapshot

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

inferred← citeshttps://doi.org/10.35631/ijirev.515012openalex-snapshot

THE SIGNIFICANCE OF PROPER COOLING TOWER WATER TREATMENT: A CASE STUDY

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.