Support vector regression and ant colony optimization for HVAC cooling load prediction

Ding, Lixing; Jinhu, Lv; Li, Xuemei; Li, Lanlan

doi:10.1109/3ca.2010.5533861

Cited by 14 publications

(4 citation statements)

References 3 publications

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“…The length of jump is calculated by using Eq. (2) Lixing et al (2010) proposed an ACO based method to predict HVAC cooling load by using support vector regression (SVR), SVR is known for its better ability to correlate inputs and outputs. However, the parameter determination of SVR is a big challenge and ACO was used to deal with this problem.…”

Section: Ant Colonymentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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Section: Ant Colonymentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…Synthetic faulty data were used to develop support vector machine (SVM) models for single and multiple FDD in air handling units (AHU), centrifugal chillers and other HVAC equipment/systems [7,[34][35][36][37][38][39][40]. Support vector regression (SVR) models were applied for single FDD [41][42][43][44][45]. b.…”

Section: Discussionmentioning

confidence: 99%

Detection and Diagnosis of Dependent Faults That Trigger False Symptoms of Heating and Mechanical Ventilation Systems Using Combined Machine Learning and Rule-Based Techniques

Bezyan

Zmeureanu

2022

Energies

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Detection and diagnosis of the malfunction of the heating, ventilation, and air conditioning (HVAC) systems result in more energy efficient systems with a higher level of indoor comfort. The information from the system combined with the artificial intelligence methods contributes to powerful fault detection and diagnosis. The paper presents a novel method for the detection and diagnosis of multiple dependent faults in an air handling unit (AHU) of HVAC system of an institutional building during heating season. The proposed method guided the search for faults, by using the information and operation flow between sensors. Support vector regression (SVR) models, developed from building automation system (BAS) trend data, predicted air temperature of two target sensors, under normal operation conditions without known problems. The fault symptom was detected when the residual of measured and predicted values exceeded the threshold. The recurrent neural network (RNN) models predicted the normal operation values of regressor sensors, which were compared with measurements, as the first step for the identification of fault symptoms. Rule-based models were used for fault diagnosis of sensors or equipment. Results from a case study of an existing building showed the quality of proposed method for the detection and diagnosis of the multiple dependent faults.

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“…In research [27], ANN, categorization and regression tree (CART), general linear regression (GLR), and chi-squared automatic interaction detector (CHAID) were used to forecast the cooling loads of the building. The networks' inputs for the prediction were the technical parameters of the building [28].…”

Section: Introductionmentioning

confidence: 99%

AI-based KNN Approaches for Predicting Cooling Loads in Residential Buildings

2024

IJACSA

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Cooling Load (CL) estimation in residential buildings is crucial for optimizing energy consumption and ensuring indoor comfort. This article presents an innovative approach that leverages Artificial Intelligence (AI) techniques, particularly K-Nearest Neighbors (KNN), in combination with advanced optimizers, including Dynamic Arithmetic Optimization (DAO) and Wild Geese Algorithm (WGA), to enhance the accuracy of CL predictions. The proposed method harnesses the power of KNN, a machine-learning algorithm renowned for its simplicity and efficiency in regression tasks. By training on historical CL data and relevant building parameters, the KNN model can make precise predictions, 768 sample with considering factors such as Glazing Area, Glazing Area Distribution, Surface Area, Orientation, Overall Height, Wall Area, Roof Area, and Relative Compactness. Two state-of-the-art optimizers, DAO and WGA, are introduced to refine the CL estimation process further. The integration of KNN with DAO and WGA yields a robust AI-driven framework proficient in the precise estimation of CL in residential constructions. This approach not only enhances energy efficiency by optimizing cooling system operations but also contributes to sustainable building design and reduced environmental impact. Through extensive experimentation and validation, this study demonstrates the effectiveness of the proposed method, showcasing its potential to revolutionize CL estimation in residential buildings. The results indicate that the hybridization of KNN with DAO optimizers yields promising outcomes in predicting CL. The high R2 value of 0.996 and low RMSE value of 0.698 demonstrate the accuracy of the KNDA model.

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Support vector regression and ant colony optimization for HVAC cooling load prediction

Cited by 14 publications

References 3 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Detection and Diagnosis of Dependent Faults That Trigger False Symptoms of Heating and Mechanical Ventilation Systems Using Combined Machine Learning and Rule-Based Techniques

AI-based KNN Approaches for Predicting Cooling Loads in Residential Buildings

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