Integrated framework for optimization of air- and water-side HVAC systems to minimize electric utility cost of existing commercial districts

Fathollahzadeh, Mohammad Hassan; Velasco, Paulo Cesar Tabares

doi:10.1016/j.enbuild.2022.112328

Cited by 11 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we conducted experiments with several predictive models, each sensitive to seasonal variations. These included the Chen's Fuzzy Time Series model, its modified version, SARIMA [16], HWES-Additive [16], HWES-Multi [16], neural network (NN) based ensemble model [16], ANN-SARIMA [17], and SARIMAX [18]. The significant advancement in this research was the refinement of the defuzzification process in Chen's Fuzzy Time Series model, which was specifically engineered to integrate seasonal variations, thereby improving the precision of the predictions.…”

Section: Prediction Modelmentioning

confidence: 99%

“…The prediction of AHU induction motor frequency involved various models, encompassing proposed model with state-of-the-art methods includes SARIMA [16], HWES-Additive [16], HWES-Multi [16], neural network (NN) based ensemble model [16], ANN-SARIMA [17], and SARIMAX [18]. The distinct model fit parameters for each prediction approach are detailed in Tables 3 through 6.…”

Section: Prediction Of Ahu Induction Motor Frequencymentioning

confidence: 99%

“…Fathollahzadeh [18] (SARIMAX). This model, designed to consider seasonal variations, incorporates input data such as weather conditions, indoor temperature, humidity, and chilled water flow rate.…”

Section: Introductionmentioning

confidence: 99%

“…Their effectiveness in capturing seasonal variations makes them preferred choices in this domain [19]. Moreover, state-of-the-art methods such as neural network based ensemble model [16], a hybrid of ANN and SARIMA [17], and SARIMAX [18] are also utilized for predicting AHU induction motor control. An additional advantage of these statistical methods is their efficiency in environments with limited data availability, as they do not require extensive datasets to perform effectively [20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

2024

IJIES

View full text Add to dashboard Cite

The purpose of this study is to introduce a novel approach to predict induction motor frequency adjustments in Air Handling Units (AHU). This is essential as traditional methods have frequently been unable to effectively address the complex seasonal and stochastic fluctuations that are inherent in these environments. To overcome this challenge, the research focuses on utilizing experimental Chen's Fuzzy Time Series model, specifically designed to incorporate temporal and seasonal patterns into the predictive analysis. Various predictive models, including Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing (HWES), neural network (NN) based ensemble model, hybrid of artificial neural network (ANN) and SARIMA, and Seasonal Autoregressive Integrated Moving Average with Exogenous factors (SARIMAX), are compared to determine the most effective model in optimizing AHU induction motor frequency. Results indicate that the modified Chen's Fuzzy Time Series model demonstrated high efficacy with an R-squared value of 0.9945 in a one-hour time interval in the seasonal pattern, showing an almost perfect fit between predicted outcomes and actual data compared to other prediction models. Furthermore, the modified Chen's model achieved a Mean Absolute Percentage Error (MAPE) of 2.41% and a Root Mean Square Error (RMSE) of 0.72, significantly outperforming other models in predictive accuracy and reliability. The modified Chen's model showed an efficiency improvement of 86% in MAPE and 87% in RMSE compared to other prediction models.

show abstract

Section: Prediction Modelmentioning

confidence: 99%

Section: Prediction Of Ahu Induction Motor Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

2024

IJIES

View full text Add to dashboard Cite

show abstract

“…Nowadays, the energy consumption in buildings has become a crucial issue due to its intensive usage. In developed countries, the domestic and commercial sectors can account for as high as 20% up to 40% of total energy usage [1]. The primary challenge lies in ensuring dynamic hydraulic balance within the variable water volume system (VWV system) to eliminate coupling effects between branches and effectively reduce operating energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Variable Pressure Difference Control Method for Chilled Water System Based on the Identification of the Most Unfavorable Thermodynamic Loop

Chen,

Han

2024

Buildings

View full text Add to dashboard Cite

A variable pressure differential fuzzy control method is proposed based on the online identification method for key parameters and the fuzzy subset inference fuzzy control method of the chilled water system network model. Firstly, a phase plane fuzzy identification method is proposed for the most unfavorable thermal loop. The study focuses on analyzing the trend of room temperature deviation and deviation change in different quadrants in the phase plane. Furthermore, we establish a chilled water pipe network model that recalculates flow variation in both the main pipe and each branch pipe section to eliminate the most unfavorable thermal loop. Finally, the test platform for the fan coil variable flow air conditioning water system was designed and constructed to meet the requirements of energy-saving regulation. Additionally, the network monitoring system for the test platform was completed. The calibration and debugging results demonstrate that the monitoring error is within ±5.0%, ensuring precise control of room temperature at the end of the branch within ±0.5 °C. Results demonstrate that our novel method exhibits superior stability in room temperature control compared to traditional linear variable pressure differential set point controls while achieving energy saving ranging from 4.7% to 6.5%.

show abstract

A parametric, control-integrated and machine learning-enhanced modeling method of demand-side HVAC systems in industrial buildings: A practical validation study

Kong,

Hong,

Yang

et al. 2025

Applied Energy

View full text Add to dashboard Cite

Integrated framework for optimization of air- and water-side HVAC systems to minimize electric utility cost of existing commercial districts

Cited by 11 publications

References 27 publications

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Variable Pressure Difference Control Method for Chilled Water System Based on the Identification of the Most Unfavorable Thermodynamic Loop

A parametric, control-integrated and machine learning-enhanced modeling method of demand-side HVAC systems in industrial buildings: A practical validation study

Contact Info

Product

Resources

About