scholarly journals Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ji Ke ◽  
Yude Qin ◽  
Biao Wang ◽  
Shundong Yang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Principal Component ◽  
Building Energy ◽  
Physical Models ◽  
Data Driven ◽  
Building Energy Management

Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms

2019 ◽  
Vol 41 (1) ◽  
pp. 108-127 ◽  
Author(s):  
Kangji Li ◽  
Wenping Xue ◽  
Gang Tan ◽  
Anthony S Denzer
Keyword(s):  
Energy Consumption ◽  
Fault Diagnosis ◽  
Evolutionary Algorithms ◽  
Energy Management ◽  
Building Energy ◽  
Data Driven ◽  
Support Vector ◽  
Building Energy Consumption ◽  
Energy Forecasting ◽  
Building Energy Management

Energy consumption forecasting for buildings plays a significant role in building energy management, conservation and fault diagnosis. Owing to the ease of use and adaptability of optimal solution seeking, data-driven techniques have proved to be accurate and efficient tools in recent years. This study provides a comprehensive review on the existing data-driven approaches for building energy forecasting, such as regression models, artificial neural networks, support vector machines, fuzzy models, grey models, etc. On this basis, the paper puts emphasis to the discussion on evolutionary algorithms hybridized models that combine evolutionary algorithms with regular data-driven models to improve prediction accuracy and robustness. Various combinations of such hybrid models are classified and their characteristics are analyzed. Finally, a detailed discussion on the advantages and challenges of current predictive models is provided. Practical Application: Building energy consumption prediction is important for building energy management, efficiency and fault diagnosis. For existing buildings, multisourced, heterogeneous or inadequate data-driven models may lead to convergence problem or poor model accuracy. To this end, a state of art review on building energy forecasting technique is helpful for related professionals in the building industry.

A framework for a multi-source, data-driven building energy management toolkit

2021 ◽  
pp. 111255
Author(s):  
Andre A. Markus ◽  
Brodie W. Hobson ◽  
H. Burak Gunay ◽  
Scott Bucking
Keyword(s):  
Energy Management ◽  
Building Energy ◽  
Data Driven ◽  
Building Energy Management ◽  
Source Data

scholarly journals Predicting Building Energy Consumption using Engineering and Data Driven Approaches: A Review

2017 ◽  
Vol 2 (5) ◽  
pp. 44 ◽  
Author(s):  
Aulon Shabani ◽  
Orion Zavalani
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Building Energy ◽  
Data Driven ◽  
World Population ◽  
Design Phase ◽  
Energy Prediction ◽  
Important Concern ◽  
Paper Review ◽  
Second Category

Rapid growth of world population has higher impact on increasing buildings energy consumption. Therefore, improving energy consumption is an important concern for building engineers and operators. Energy management through forecasting approaches as one of most effective methods is in focus of this paper. Review of most elaborated methods is in our focus, where we investigate two main directions of energy prediction approaches. First category of approaches focuses on engineering methods mainly very reliable on building early operation stages and design phase, meanwhile second category go through data driven methods. Existing research works focused on these two models are introduced emphasizing advantages and relevant applications of methods.

scholarly journals An Ontology-Based Framework for Building Energy Management with IoT

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 485 ◽  
Author(s):  
Clement Lork ◽  
Vishal Choudhary ◽  
Naveed Ul Hassan ◽  
Wayes Tushar ◽  
Chau Yuen ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Energy Savings ◽  
Environmental Parameters ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Commercial Building ◽  
Building Energy Management ◽  
Building Management

In this paper, we develop an ontology-based framework for energy management in buildings. We divide the functional architecture of a building energy management system into three interconnected modules that include building management system (BMS), benchmarking (BMK), and evaluation & control (ENC) modules. The BMS module is responsible for measuring several useful environmental parameters, as well as real-time energy consumption of the building. The BMK module provides the necessary information required to understand the context and cause of building energy efficiency or inefficiency, and also the information which can further differentiate normal and abnormal energy consumption in different scenarios. The ENC module evaluates all the information coming from BMS and BMK modules, the information is contextualized, and finally the cause of energy inefficiency/abnormality and mitigating control actions are determined. Methodology to design appropriate ontology and inference rules for various modules is also discussed. With the help of actual data obtained from three different rooms in a commercial building in Singapore, a case study is developed to demonstrate the application and advantages of the proposed framework. By mitigating the appropriate cause of abnormal inefficiency, we can achieve 5.7%, 11.8% and 8.7% energy savings in Room 1, Room 2, and Room 3 respectively, while creating minimum inconvenience for the users.

scholarly journals A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Cheng Fan ◽  
Meiling Chen ◽  
Xinghua Wang ◽  
Jiayuan Wang ◽  
Bufu Huang
Keyword(s):  
Energy Management ◽  
Data Science ◽  
Data Preprocessing ◽  
Building Energy ◽  
Data Driven ◽  
Quality Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Building Energy Management ◽  
Operational Data

The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field.

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