Young Tae Chae

Bio: Young Tae Chae is an academic researcher from Cheongju University. The author has contributed to research in topics: Energy consumption & Efficient energy use. The author has an hindex of 9, co-authored 22 publications receiving 616 citations. Previous affiliations of Young Tae Chae include IBM & Hanyang University.

Estimating building thermal properties by integrating heat transfer inversion model with clustering and regression techniques for a portfolio of existing buildings

Abstract: A static heat transfer model is derived from a system of dynamic equations by integrating the dynamic equations over different time periods. That static heat transfer model links periodic (e.g., monthly) energy usage with cooling and heating degree hours, humidifying and dehumidifying hours. Its coefficients of measuring correlations correspond to the thermal parameters of buildings. Temporal data from a building may be used to estimate the overall heat transfer parameters. A clustering scheme may be developed to decompose all the buildings into different clusters based on one or more similarity criteria. The overall heat transfer parameters are separated into values for the wall, roof and window using multiple buildings' data in the same cluster or group.

A review of data-driven building energy consumption prediction studies

Abstract: Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review on time series forecasting techniques for building energy consumption

Abstract: Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.

A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

Abstract: Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.