Comparative Evaluation of Building Cooling Load Prediction Models with Multi-Layer Neural Network Learning Algorithms

Seong, Nam-Chul; Hong, Goopyo

doi:10.12813/kieae.2022.22.4.035

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine-learning-based prediction studies typically evaluate predictive performance based on the Measurement and Verification (M & V) Guidelines of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) [33], the United States Department of Energy's Federal Energy Management Program (FEMP) [34], and the International Performance Measurement and Verification Protocol [35][36][37]. Each of these sources suggests measurement and verification protocols and predictive performance criteria for prediction models.…”

Section: Predictive Performance Criteriamentioning

confidence: 99%

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Lee,

Cho,

Park

et al. 2024

Energies

View full text Add to dashboard Cite

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.

show abstract

Section: Predictive Performance Criteriamentioning

confidence: 99%

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Lee,

Cho,

Park

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…In South Korea, recent studies have been conducted to predict building energy load [18,19], consumption [20,21], energy usage patterns [22,23], etc. by using machine learning or artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term

Hong,

Seong

2023

Buildings

Self Cite

View full text Add to dashboard Cite

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30% of total energy has been consumed by buildings, and much attention has been paid to reducing building energy consumption. There are many ways to reduce building energy consumption. One of the most relevant methods is machine learning. While machine learning methods provide accurate energy consumption predictions, they require huge datasets. The present study developed an artificial neural network (ANN) model for building energy consumption predictions with small datasets. As mechanical systems are the most energy-consuming components in the building, the present study used the energy consumption data of a direct-fired absorption chiller for the short term. For the optimization, the prediction results were investigated by varying the number of inputs, neurons, and training data sizes. After optimizing the ANN model, it was validated with the actual data collected through a building automation system. In sum, the outcome of the present study can be used to predict the energy consumption of the chiller as well as improve the efficiency of energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets, which can improve the efficiency of building energy management.

show abstract

Optimization of the ANN model for Energy Consumption Prediction of Direct Fired Absorption Chiller for a Short-Term

Hong,

Seong

2023

Preprint

View full text Add to dashboard Cite

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

show abstract

Comparative Evaluation of Building Cooling Load Prediction Models with Multi-Layer Neural Network Learning Algorithms

Cited by 3 publications

References 12 publications

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term

Optimization of the ANN model for Energy Consumption Prediction of Direct Fired Absorption Chiller for a Short-Term

Contact Info

Product

Resources

About