The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency (original) (raw)

An overview of machine learning applications for smart buildings

Sustainable Cities and Society

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings' adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation. 'intelligent building' (IB) and 'smart building' (SB) (Al Dakheel et al. (2020); Wang et al. (2020))). A shift towards the implementation of artificial intelligence (AI) trained by machine learning algorithms is recognized as one of the major trends of development (Karpook, 2017). Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced useful for the autonomous control networks of buildings (Han et al., 2019). Quite a few review articles have been published with various perspectives on smart buildings. A quick look at the most relevant review articles in the field reveals that most of them focus on issues such as hardware technologies, monitoring, forecasting, modelling, building energy management, and applications of machine learning (Alawadi et al.

IOT and Machine Learning based Building Energy Management

One unit of energy saved is two units of energy produced. There is more talk on new energy generation and renewable energy than Energy conservation. This paper implements the building energy management and conservation using advanced technologies like Internet of Things (IOT) and Machine Learning. This paper mainly focuses on energy conservation through building automation and energy management through sensors and actuators using advanced IOT technologies. The building appliances are sensed through various types of sensors and controlled using Arduino for optimal use of electricity in the building. Monitoring and control of all the electrical appliances of the building is done through the handheld devices. The unnecessary use of electricity due to carelessness of the customers is identified and controlled remotely using the mobile application. As the energy consumption is monitored continuously all the time which eventually brings awareness about use of energy. Machine learning algorithms are used for accurate load forecasting leading to effective building energy management.

Machine Learning for Smart and Energy-Efficient Buildings

arXiv (Cornell University), 2022

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

Machine Learning for Smart Building Applications: Review and Taxonomy

2018

The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building ma...

IRJET- Smart Building Energy Management System using Machine Learning and IoT

IRJET, 2021

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO2 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. Buildings are currently responsible for more than 40% of global energy and one third of global greenhouse gas emissions. This project work aims to develop a machine learning model, method, architecture or appliance to reduce building energy use and emissions using a smart sensor for residential or commercial buildings. An experienced operator can do a good job of adjusting set points and schedule. But no matter how good they are, a human's ability is limited by the amount of data he or she can process. Significant opportunities exist to take advantage of external data sources including real-time occupancy sensor networks, changing space schedules, weather forecasts, grid carbon intensity, and other environmental conditions that could help us better predict space set-points and schedules 24x7. The sensor senses CO2 reading, sound level, ambient light, door state sensing etc. These can be used to accurately estimate the number of occupants in each room using machine learning techniques and this technique can be used to predict future occupancy. In this project we take a look at some questions regarding the construction and the exploitation of knowledge related to different types of buildings in order to optimize the use of different resources while still assuring the occupants' comfort. The Heating, ventilation and Air conditioning (HVAC) equipments data of the building are captured using IOT sensors and the vast data collected using these sensors are analyzed using Machine Learning based Big data analysis technique and the decision obtained is used to control HVAC devices automatically. We design an IOT based Big data analysis model that characterize a building as smart and finally, it is planned to make Electrical Department of our Institution smart with our SB model.

Machine Learning, Big Data, And Smart Buildings: A Comprehensive Survey

2019

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

The Role of ML, AI and 5G Technology in Smart Energy and Smart Building Management

Electronics

With the help of machine learning, many tasks can be automated. The use of computers and mobile devices in “intelligent” buildings may make tasks such as controlling the indoor climate, monitoring security, and performing routine maintenance much easier. Intelligent buildings employ the Internet of Things to establish connections among the many components that make up the structure. As the notion of the Internet of Things (IoT) gains attraction, smart grids are being integrated into larger networks. The IoT is an integral part of smart grids since it enables beneficial services that improve the experience for everyone inside and individuals are protected because of tried-and-true life support systems. The reason for installing Internet of Things gadgets in smart structures is the primary focus of this investigation. In this context, the infrastructure behind IoT devices and their component units is of the highest concern.

Trending machine learning models in cyber‐physical building environment: A survey

WIREs Data Mining and Knowledge Discovery

Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber‐physical system (CPS) researchers have put forth associated research questions to reduce cyber‐physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensive...

Improving Energy Consumption of a Commercial Building with IoT and Machine Learning

IT Professional, 2018

The critical requirements for devices connected to the Internet of Things (IoT) are long battery life, long coverage range, and low deployment cost. In this work, we developed a machine learning based smart controller for the HVAC of commercial building using LoRa and compared it with short range RF communication in an indoor setting. The comparison was made in terms of battery life, coverage range and memory size. The effect of changing the transmission power of LoRa on battery consumption of the sensor node was also evaluated. Results show that coverage range of LoRa was 60.4% more than short range communication inside a building. The smart controller was capable of identifying when the room was unoccupied and turning off the HVAC which reduced the energy consumption up to 19.8%. Introduction According to Cisco [1], 50 billion devices will be connected to the internet by 2020. Different types of devices can be connected to the internet from small devices (RFIDs, Sensors) to large devices like TVs, Cameras etc., and mobile devices like vehicles. The Internet of Things (IoT) interconnects these devices and exchanges data between these devices. Therefore, Machine to Machine (M2M) communication is required for exchange of data between devices in IoT. Communication between devices in IoT has already been done by multi-hop short range communication (ZigBee, Bluetooth and RF communication) [2]-[4]. Short range communication (Zigbee, Bluetooth, RF communication) operates in unlicensed ISM bands centred at 2.4 GHz, 868/915 MHz. 433 MHz and 169 MHz. The coverage of these short-range communication (unidirectional or bi-directional) is usually in few meters but they can achieve high data rate. In applications where the distance between sensor nodes and base station is large, short range communication standards are not feasible. More recently industry has been developing in low power wide area networks (LPWAN). LPWAN is introduced as a promising alternative between multi-hop short range communication which operates in unlicensed frequency band and long range cellular communication which operates in licensed frequency band. Basic requirements for LPWAN are long coverage, less power consumption, low deployment cost, low device cost, support large number of devices and easily expandable [5]. Long range (LoRa) alliance [6], Sigfox [7], and Weightless [8] are examples of LPWAN. In our previous work [9], we implemented the smart controller for heating ventilation and cooling (HVAC) of commercial building. Random neural networks (RNN) were used for machine learning of