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Site (a) John Jay new building north and south towers at center with buildings to south and north. (b) South tower floor plan with zone numbers.
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Building thermal response rates refer to those dynamic conditions when a building interior is not held in steady-state by the operation of its HVAC systems. A method is proposed and preliminary results demonstrated for using data available from the typical large-building Building Automation System (BAS) to automatically characterize temperature res...
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Used and developed initially for the IT industry, the Cloud computing and Internet of Things concepts are found at this moment in a lot of sectors of activity, building industry being one of them. These are defined like a global computing, monitoring and analyze network, which is composed of hardware and software resources, with the feature of allo...
Citations
... Many software products have been developed based on physical models, including EnergyPlus, DOE-2, TRNSYS, and eQUEST [9]. However, physical models present some deficiencies such as professional knowledge requirements, and the high costs of data collection and computation [10]. For green buildings with many energysaving technologies, many systems are involved. ...
Integrating clustering with regression has gained great popularity due to its excellent performance for building energy prediction tasks. However, there is a lack of studies on finding suitable regression models for integrating clustering and the combination of clustering and regression models that can achieve the best performance. Moreover, there is also a lack of studies on the optimal cluster number in the task of short-term forecasting of building energy consumption. In this paper, a comprehensive study is conducted on the integration of clustering and regression, which includes three types of clustering algorithms (K-means, K-medians, and Hierarchical clustering) and four types of representative regression models (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Artificial Neural Network (ANN), and extreme gradient boosting (XGBoost)). A novel performance evaluation index (PI) dedicated to comparing the performance of two prediction models is proposed, which can comprehensively consider different performance indexes. A larger PI means a larger performance improvement. The results indicate that by integrating clustering, the largest PI for SVR, LASSO, XGBoost, and ANN is 2.41, 1.97, 1.57, and 1.12, respectively. On the other hand, the performance of regression models integrated with clustering algorithms from high to low is XGBoost, SVR, ANN, and LASSO. The results also show that the optimal cluster number determined by clustering evaluation metrics may not be the optimal number for the ensemble model (integration of clustering and regression model).
... However, in general, it is challenging to develop accurate physical models for predicting energy consumption due to the complexity and uncertainty of relationships among influencing variables [11]. In addition, it is typically a time-consuming process which demands extensive knowledge of buildings and their operational characteristics [9]. Results based on these engineering methods show significant variations [8] and may not be ideal to model can hardly model dynamic building thermal characteristics and occupant behavior effectively [5]. ...
In 2018, commercial buildings accounted for nearly 18.2% of the total energy consumption in the USA, making it a significant contributor to the greenhouse gases emissions. Specifically, office buildings accounted for 14% of the energy usage by the commercial sector. Hence, their energy performance has to be closely monitored and evaluated to address the critical issues of greenhouse gases emissions. Several data-driven statistical and machine learning models have been developed to assess the energy performance of office buildings based on historical data. While these methods often provide reliable prediction accuracy, they typically offer little interpretation of the relationships between variables and their impacts on energy consumption. Moreover, model interpretability is essential to understand, control and manage the variables affecting the energy consumption and therefore, such a feature is crucial and should be emphasized in the modelling procedure in order to obtain reliable and actionable results. For this reason, we use generalized additive models as a flexible, efficient and interpretable alternative to existing approaches in modeling and predicting the energy consumption in office buildings. To demonstrate the advantages of this approach, we consider an application to energy consumption data of HVAC systems in a mixed-use multi-tenant office building in Chicago, Illinois, USA. We present the building characteristics and various influential variables, based on which we construct a generalized additive model. We compare the prediction performance using various commonly used calibration metrics between the proposed model and existing methods, including support vector machine as well as classification and regression tree. We find that the proposed method outperforms the existing approaches, especially in terms of short term prediction.
The design of a building’s envelope can considerably affect the building’s energy performance. However, finding the best building envelope parameters to achieve the optimal design in respect to the energy performance is a complicated task. Therefore, some different methods have been developed to optimize building envelope parameters to achieve better thermal behaviour and energy consumption. Many types of smart claddings and kinetics have been developed, but their details remain unclear and unstructured. The smart cladding discussed in this work is defined as an integrated sustainable tool to control energy consumption regarding comfort parameters. These parameters directly affect the occupant’s behaviour, and considering these parameters helps to achieve better practical results. This paper aims to design a control system process and its supporting theory, to develop a model of smart cladding. In order to clarify the theory (methodology), related calculations are provided based on formulations. The obtained data was validated by the carrier hourly analysis program (HAP) v4.90. During the design of the mentioned control system, effective parameters are investigated; such as glazing ratio, visual comfort and the effect of dwelling occupation on heat transfer. The research took into account both full-time and part-time employment as two separate time periods during the day. The energy consumption was evaluated regarding different parameters, such as visual comfort and occupation period; and the results show a heat transfer reduction of approximately half. Consequently, the smart cladding’s design increased energy savings by 45%.
Unlike burning fossil fuels, energy generated through wind and solar cannot be synchronised to demand. To capture surplus energy generated by renewable sources, comprehensive energy storage that can be dispatched during generation shortages is required. There is substantial research on identifying the relevant qualities for energy storage technology for a given application and on comparing existing technologies against each other. However, the literature has not adequately considered passive thermal energy storage in buildings as an energy storage option. This paper demonstrates that passive thermal energy storage in buildings should be included as another form of energy storage. We also show that the performance of passive storage can be measured using existing metrics used to evaluate conventional energy storage technologies. The current state of the art regarding the use of passive thermal energy storage in buildings is also reviewed, along with commercial opportunities in Australian energy markets.
