State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Methods to Obtain the Occupant Perspective

This chapter summarizes the most important methods for actively engaging occupants in the processes of designing buildings. Each stage in the building life cycle places different demands on the professional-to-occupant relationship. Both objective and subjective data are important in this relationship and raises key epistemological questions about factors that cannot be directly observed—e.g., how do we know what we know about occupant behavior? The chapter guides the reader through this intellectually dangerous terrain by suggesting that the best way to find out what people think is to ask them. Some methods discussed here are familiar to practitioners, including interviews, surveys, focus groups, and direct observation. Others are just entering widespread practice, including virtual reality simulations, ubiquitous sensors and monitoring systems, and momentary ecological assessments. Each method has strengths, weaknesses, and appropriateness for use during certain stages of the building life cycle. The key takeaways from this chapter are that (1) building designers and operators can learn much value from occupants and (2) the new skills needed to engage successfully can be quickly learned. Occupant-centric design approaches that employ these methods improve the likelihood of successful building, interface design, and occupant outcomes

Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

This book promotes occupants as a focal point for the design process

Context-Aware Design of Cyber-Physical Human Systems (CPHS)

Recently, it has been widely accepted by the research community that interactions between humans and cyber-physical infrastructures have played a significant role in determining the performance of the latter. The existing paradigm for designing cyber-physical systems for optimal performance focuses on developing models based on historical data. The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models. As a result, many existing models do not or only partially address those context factors of a new design owing to the lack of capabilities to capture the context factors. This limitation in many existing models often causes performance gaps between predicted and measured results. We envision a new design environment, a cyber-physical human system (CPHS) where decision-making processes for physical infrastructures under design are intelligently connected to distributed resources over cyberinfrastructure such as experiments on design features and empirical evidence from operations of existing instances. The framework combines existing design models with context-aware design-specific data involving human-infrastructure interactions in new designs, using a machine learning approach to create augmented design models with improved predictive powers.Comment: Paper was accepted at the 12th International Conference on Communication Systems and Networks (COMSNETS 2020

A systematic literature review using text mining and bibliometric analysis

109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.publishersversionpublishe

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

[EN] This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings

Optimized energy and air quality management of shared smart buildings in the covid-19 scenario

Worldwide increasing awareness of energy sustainability issues has been the main driver in developing the concepts of (Nearly) Zero Energy Buildings, where the reduced energy consumptions are (nearly) fully covered by power locally generated by renewable sources. At the same time, recent advances in Internet of Things technologies are among the main enablers of Smart Homes and Buildings. The transition of conventional buildings into active environments that process, elaborate and react to online measured environmental quantities is being accelerated by the aspects related to COVID-19, most notably in terms of air exchange and the monitoring of the density of occupants. In this paper, we address the problem of maximizing the energy efficiency and comfort perceived by occupants, defined in terms of thermal comfort, visual comfort and air quality. The case study of the University of Pisa is considered as a practical example to show preliminary results of the aggregation of environmental data

Learning and energy-efficient renovation of residential buildings – The user perspective

Renovation of residential buildings has gained more attention in recent years. There are several challenges connected to energy efficient renovation of existing buildings. European Commission acknowledges the challenges and emphasizes the need to replicate best practices and learn from lessons. Several research studies have been conducted on the topic of energy-efficient renovation of residential buildings, some of which have highlighted significant aspects that are to be considered in the renovation efforts / projects. One of the aspects is user perspective that encompasses, among other things, user involvement and user behaviour. Knowledge sharing and learning play a significant role in developing and ensuring the user behaviour that is suitable for harvesting the intended benefits of energy-efficient renovation efforts. There can be wonderful energy-efficient solutions that can reduce greenhouse gas emission and promote sustainability. But, if the solutions are not used appropriately by the user, then it will hinder achieving the expected positive effect. As a result, developing and implementing the solutions in the first place will lose its very purpose. This paper looks at the user perspective in energy-efficient renovation of residential buildings and describe the learning-points associated with it. Focusing on these learning-points and the user perspective itself has its implication on wider issues such as policy making and future energy consumption. In addition, this paper summarises basic knowledge on understanding users' behaviour regarding energy and consumption, as well as briefly describes tools and examples of research projects that incorporate the users' perspective in their design. This paper is based on narrative literature study. It touches upon aspects related to the Sustainable Development Goals 11 (Sustainable cities and communities) and 13 (Climate action), at least to a certain extent. This paper is connected to an EU-project called "REZBUILD" (https://rezbuildproject.eu/), which focuses on developing decision and planning support for accomplishing near zero-emission in renovation of residential buildings.publishedVersio