Research on thermal load prediction of district heating station based on transfer learning

Wang, Chendong; Yuan, Jianjuan; Huang, Ke; Zhang, Ji; Zheng, Lihong; Zhou, Zhi‐Hua; Zhang, Yufeng

doi:10.1016/j.energy.2021.122309

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…The less resilient individuals will be progressively eliminated from the population as the algorithm iterations continue on, according to the standards given in Eq. (13). At the end of the last iteration, the population size will finally reach the final number, 𝑁 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 , after decreasing linearly over time.…”

Section: A) Phase Of Velocity Displacement and Migrationmentioning

confidence: 99%

“…Artificial Intelligence, particularly Machine Learning (ML) [12], has emerged as a potent tool for addressing complex challenges across various domains. In the context of cooling load estimation, ML algorithms shine as they have the capacity to assimilate vast datasets encompassing diverse parameters such as outdoor temperatures [13], humidity levels, occupancy patterns, and architectural features. Among the myriad of ML algorithms, the K-Nearest Neighbors (KNN) algorithm stands out for its simplicity and effectiveness in regression tasks [14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A) Phase Of Velocity Displacement and Migrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2024

IJACSA

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“…This significant energy usage in the building sector has positioned carbon emissions (CO2) as a primary driver of climate change, global warming, and air pollution [4], [5]. Consequently, many architects, researchers, and engineers have taken up the task of investigating models that centre on building envelopes and design features with the goal of reducing the negative effects associated with energy consumption in buildings [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Reliable Hybridization Approach for Estimation of The Heating Load of Residential Buildings

2024

IJACSA

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In recent times, the world's growing population, coupled with its ever-increasing energy demands, has led to a significant rise in the consumption of fossil fuels. Consequently, this surge in fossil fuel usage has exacerbated the threat of global warming. Building energy consumption represents a significant portion of global energy usage. Accurately determining the energy consumption of buildings is crucial for effective energy management and preventing excessive usage. In pursuit of this goal, this study introduces a novel and robust machine learning (ML) method based on the K-nearest Neighbor (KNN) algorithm for predicting the heating load of residential buildings. While the KNN model demonstrates satisfactory performance in predicting heating loads, for the attainment of optimal results and accuracy, two novel optimizers, the Snake Optimizer (SO) and the Black Widow Optimizer (BWO), have been incorporated into the hybridization of the KNN model. The results highlight the effectiveness of KNSO in predicting heating load, as evidenced by its impressive R 2 value of 0.986 and the low RMSE value of 1.231. This breakthrough contributes significantly to the ever-pressing pursuit of energy efficiency in the built environment and its pivotal role in addressing global environmental challenges.

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“…ANNs have been used to support parameter prediction, e.g., natural gas load prediction [ 12 ], solar radiation potential [ 13 ], or thermal loads in regional energy systems [ 14 , 15 ]; process optimization, e.g., the parameter optimization of industrial gas turbines [ 16 ] or biodiesel-based engine performance [ 17 ]; and process control, e.g., fault detection [ 18 ] or steam turbine heating [ 19 ]. ANN models and their improved forms have gradually become more widely used in flowmeter verification, calibration, and measurement accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

Research on a Hybrid Intelligent Method for Natural Gas Energy Metering

Dong

Song

He³

et al. 2023

Sensors

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In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements’ accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%.

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Research on thermal load prediction of district heating station based on transfer learning

Cited by 21 publications

References 21 publications

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

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

Reliable Hybridization Approach for Estimation of The Heating Load of Residential Buildings

Research on a Hybrid Intelligent Method for Natural Gas Energy Metering

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