Towards Data-Driven Simulation Models for Building Energy Management

2021 ◽  
pp. 401-407
Author(s):  
Juan Gómez-Romero ◽  
Miguel Molina-Solana
Keyword(s):  
Energy Management ◽  
Simulation Models ◽  
Building Energy ◽  
Data Driven ◽  
Building Energy Management

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 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.

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.

Data-Driven Edge Computing: A Fabric for Intelligent Building Energy Management Systems

2021 ◽  
pp. 2-10
Author(s):  
Zhishu Shen ◽  
Jiong Jin ◽  
Tiehua Zhang ◽  
Atsushi Tagami ◽  
Teruo Higashino ◽  
...  
Keyword(s):  
Energy Management ◽  
Building Energy ◽  
Edge Computing ◽  
Data Driven ◽  
Management Systems ◽  
Intelligent Building ◽  
Energy Management Systems ◽  
Building Energy Management

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.

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