A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings

Chaudhuri, Tanaya; Soh, Yeng Chai; Li, Hua; Xie, Lihua

doi:10.1016/j.apenergy.2019.04.065

Cited by 166 publications

(57 citation statements)

References 26 publications

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“…Reference [27] proposed a long short-term memory neural network to adequately consider the uncertain prices in electricity markets. Neural networks played a significant role in balancing between the thermal comfort and energy use in buildings [28], achieving optimal dispatch in ancillary services market [29] and reduced load curtailments [16]. In [30], a datadriven model was used to increase the learning ability for price responsive behaviors.…”

Section: A Category 1 Boundary Parameter Improvementmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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Section: A Category 1 Boundary Parameter Improvementmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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“…Cosma and Simha introduced a non-invasive approach for automatic prediction of personal thermal comfort for real-time feedback with ML [14]. Chaudhuri et al proposed an indoor-climate control framework to decrease the disparity between energy-efficiency and indoor thermal-comfort in buildings, which comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm [15]. Lu et al built a data-driven simulation model for thermal comfort based on k-nearest neighbor (KNN), random forest (RF) and SVM, which they simulate set-point control system with [16].…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Zhao

Genovese

2020

Future Internet

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With the improvement of technologies, people's demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.Future Internet 2020, 12, 30 2 of 18 prediction, and the error is relatively large. In recent years, the development trends of computer science, applied mathematics, statistics, and semiconductor hardware have brought iterative progress in artificial intelligence technology. This makes the simulation, analysis, prediction. Literature ReviewBig data mainly refers to data management on a certain scale. Due to the increase of the data volume, speed, and type, traditional methods cannot be used to proceed [1]. The artificial intelligence technology represented by machine learning and deep learning algorithms has a great dependence on datasets. Only when the original data is large enough can the trained algorithm be more accurate. Therefore, before applying the model, it is necessary to obtain enough data for repeated training.In the traditional building with related planning and landscape fields, big data was firstly applied to Geographic Information System (GIS), including a variety of spatial information such as land conditions, terrain, and climate. As for the single building, its automated development is mainly reflected in the field of intelligent building, with small but higher accuracy. The complexity of intelligent building systems is high. Meanwhile, the physical structure, indoor environment, and operating systems of the entire building are monitored and analyzed. It can develop the building in the direction of intelligence. With the help of building big data system, it can analyze the use of the building in real-time, integrate the related information of the surroundings, and let the building make optimal adjustments according to the actual situation [2]. In the process of optimal adjustment, IoT sensors and controllers are usually targeted in the building. The organic combination of physical devices and computer algorithms pla...

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“…Jazizadeh and Jung [52] report a case study where HVAC are activated according to a personalized thermal comfort measurement by an RGB video image. Zhang et al [53,54] introduce an algorithm to correlate thermal comfort, indoor airspeed and air conditioning system, while Chaudahuri et al [55] adopt an artificial neural network within the same scope. Unfortunately, none of the mentioned studies includes the hospital typology as a case study, that still remains a lack in the specific scientific literature.…”

Section: Scientific Literaturementioning

confidence: 99%

“…The survey was prepared following standards ISO 10551 [57]. Several studies adopt the subjecting approach by submitting surveys [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]. Sometimes, particular conditions or particular users, such as children in a 4-5 years age range [15,63], require the survey to be integrated or adapted to facilitate the understanding and the filling procedure.…”

Section: Subjective Approach: Thermal Comfort Surveymentioning

confidence: 99%

Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence

2019

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Despite studies on thermal comfort being consolidated in the scientific literature, people’s well-being in some specific conditions and places, such as hospitals, requires to be further explored. The paper describes the methodological approach adopted to evaluate thermal comfort level and perception of pregnant women hosted in the obstetric ward of a test-bed case (Sant’Orsola hospital in Bologna, Italy). The methodology adopts a mixed approach that compares the results of on-site monitoring by probe (as quantitative data) with the ones of a survey (questionnaire form) delivered to the involved subjects (as qualitative data) to understand if metabolic alteration may influence the pregnant women’s perception of comfort conditions. The first follows ISO 7730, the second, ISO 10551. The comparison between the instrumental collected data and the outcomes of the survey revealed a wide gap between TSV (Thermal Sensation Vote) and PMVm (Predicted Mean Vote, measured on-site). The reason can be identified in the use of a standardized metabolic unit from ISO that does not correctly reflect the physiologic condition of pregnant women. Following a trial and error methodology, a met value for pregnant women is accordingly proposed. Moreover, an adaptive thermal comfort approach is adopted. This research is a first step towards the definition of specific thermal comfort in a hospital ward hosting pregnant women and more generally offers a reflection about the need to define specific met in the standards for some particular categories (children, elderly, pregnant women, etc.) when investigating thermal comfort.

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A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings

Cited by 166 publications

References 26 publications

Review of Learning-Assisted Power System Optimization

Review of Learning-Assisted Power System Optimization

Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI

Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence

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