Energy Consumption Optimization for Heating, Ventilation and Air Conditioning Systems Based on Deep Reinforcement Learning

Yi, Peng; Shen, H. F.; Tang, X.C.; Zhang, Sizhe; Zhao, Jianye; Liu, Yuru; Nie, Yuming

doi:10.1109/access.2023.3305683

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…The CNN deep neural network model is used to predict the building performance, and it can abolish problems such as the low prediction accuracy of traditional data-driven models. Meanwhile, CNNs with backpropagation algorithms can automatically adjust the network parameters to minimize the loss function, thus improving the performance of the network, i.e., it can enhance the predictive performance of buildings and minimize the time cost [64][65][66][67][68][69][70][71][72][73][74][75][76][77]. For example, the following scholars have conducted relevant studies at this level: Yue et al investigated the application of data-driven modeling to building energy consumption and indoor environments by studying.…”

Section: At the Machine Learning Levelmentioning

confidence: 99%

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

Liu,

Wang,

Huang

2024

Sustainability

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The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the “3060 dual-carbon” goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building’s energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines.

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Section: At the Machine Learning Levelmentioning

confidence: 99%

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

Liu,

Wang,

Huang

2024

Sustainability

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“…The process of creating this model utilizes a combination of methods from deep learning, namely long short-term memory (LSTM) and a 1-dimensional convolutional neural network (1D-CNN) [21]. Combining these two methods aims to increase the accuracy of predicting electrical energy consumption [22]. The choice of these two methods is based on the temporal or sequential data type.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model

Aryanto,

Maneetham,

Crisnapati

2023

Applied Sciences

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This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.

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Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

Wu,

Ren,

Tan

et al. 2024

Build. Simul.

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Energy Consumption Optimization for Heating, Ventilation and Air Conditioning Systems Based on Deep Reinforcement Learning

Cited by 5 publications

References 39 publications

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model

Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network

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