A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

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Stroke Risk Prediction with Machine Learning Techniques

Short-term building energy predictions serve as one of the fundamental tasks in building operation management. While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions, few studies have addressed the potential data shortage problem in developing data-driven models. One promising solution is data augmentation, which aims to enrich existing building data resources for reliable predictive modeling. This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively. Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings. The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples, which in turns helps to enhance the accuracy in short-term building energy predictions. The average performance enhancement ratios in terms of CV-RMSE range between 12% and 18%. Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data. The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

Demand response (DR) of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids. In this special fast DR event, effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment. This study, therefore, developed a data-driven model predictive control (MPC) using support vector regression (SVR) for fast DR events. According to the characteristics of fast DR events, the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance. Meanwhile, a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls. Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously. Compared with RC-based MPC, the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition

A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings

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 present the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The insights obtained are helpful for the development of data-driven research in the building field.

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

The wide adoption of Building Automation System (BAS) and Building Energy Management System (BEMS) has provided building professionals with large amounts of building operational data for knowledge discovery. Considering the intrinsic complexity in building operations and common faults in data collections, data preprocessing has been recognized as an indispensable step in building operational data analysis. It can be used to enhance data quality by removing outliers and missing values, ensure data compatibility with data mining algorithms, and improve the sensitivity and reliability in data analysis. This study provides a comprehensive review on data preprocessing techniques in analyzing massive building operational data. The paper firstly reviews techniques for conventional data preprocessing tasks, including missing value imputation, outlier detection, data scaling, reduction and transformation. Afterwards, the paper proposes promising techniques for advanced data preprocessing tasks, including data partitioning, feature engineering, data augmentation and transfer learning. Based on the critical review, future research directions and potential applications for building data analysis has been summarized. This paper can provide a general picture on data preprocessing methods for building operational data analysis. The insights obtained are valuable for the development of advanced data-driven solutions for smart building energy management.

Meiling Chen

Papers

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

A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

A Critical Review on Data Preprocessing Techniques for Building Operational Data Analysis