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 of data-driven building energy consumption prediction studies

Annual Energy Outlook

In the European Union, the building sector is one of the largest energy consumer with about 40% of the final energy consumption. Reducing consumption is also a sociological, technological and scientific matter. New methods have to be devised in order to support building professionals in their effort to optimize designs and to enhance energy performances. Indeed, the research field related to building modelling and energy performances prediction is very productive, involving various scientific domains. 
Among them, one can distinguish physics-related fields, focusing on the resolution of equations simulating building thermal behaviour and mathematics-related ones, consisting in the implementation of prediction model thanks to machine learning techniques. This paper proposes a detailed review and discussion of these works. First, the approaches based on physical (‘‘white box’’) models are reviewed according three-category classification. Then, we present the main machine learning (‘‘black box’’) tools used for prediction of energy consumption, heating/cooling demand, indoor temperature. Eventually, a third approach called hybrid (‘‘grey box’’) method is introduced, which uses both physical and statistical techniques. The paper covers a wide range of research works, giving the base principles of each technique and numerous illustrative examples

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State of the art in building modelling and energy performances prediction: A review

https://escholarship.org/content/qt8755h7rn/qt8755h7rn.pdf?t=p0jvvs

Occupant behavior modeling for building performance simulation: Current state and future challenges:

Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.

Urban building energy modeling – A review of a nascent field

Towards adoption of building energy simulation and optimization for passive building design: A survey and a review

An in-depth understanding of building energy use requires a thorough understanding of human behavior. This research gives a quantitative description of human behavior in residential buildings. This quantitative description method can be used to forecast the impact of the human behavior on the indoor building environment and energy use. Human behavior influences the energy use directly and indirectly by changing window openings, air-conditioner usage, lighting, etc. This quantitative description method describes these behavioral effects. Behavior can be divided into several types according to the usage with time related, environmentally related and random modes used to quantitatively describe the behavior. The method is then applied to describe a Beijing household with comparison to on-site observations of the resident’s behavior and measurements of energy use to validate the method. The results show that the human behavior in the real world can be quantified by the quantitative description method. These simulation tools can greatly facilitate building energy conservation by describing the influence of human behavior on building performance and energy use.

https://www.springer.com/cda/content/document/cda_downloaddocument/2.pdf?SGWID=0-0-45-1377538-0

Quantitative description and simulation of human behavior in residential buildings

https://escholarship.org/content/qt4gg7k96x/qt4gg7k96x.pdf?t=p0hgt7

Xin Zhou

Papers

Towards adoption of building energy simulation and optimization for passive building design: A survey and a review

Quantitative description and simulation of human behavior in residential buildings

Data analysis and stochastic modeling of lighting energy use in large office buildings in China

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

Investigation and analyses of residential heating in the HSCW climate zone of China: Status quo and key features