Energy Efficiency In Smart Buildings Research Articles

IoT—A Promising Solution to Energy Management in Smart Buildings: A Systematic Review, Applications, Barriers, and Future Scope

The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.

An overview on building-integrated photovoltaics: technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing, and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Mapping out focus for circular economy business models (CEBMs) research in construction sector studies – a bibliometric approach

Purpose The circular economy business models (CEBMs) provide ways for firms operating in the construction industry to move from a linear to a circular approach. Thus, this study aims to explore CEBM research within the construction sector to show the focus area of studies, highlighting new areas that require attention. Design/methodology/approach This study adopted a bibliometric approach, using the Scopus database as the data source. The keywords used for paper extraction from the database were “circular economy business” OR “circular business” AND “model” OR “models” AND “construction industry” OR “building industry”. The VOSviewer software was then used to prepare a co-occurrence and co-authorship map based on the bibliographic data gathered. Findings The study’s findings reveal five research clusters in the construction industry. These clusters include circular construction intelligence, modular business modelling, eco-construction, sustainable construction economics and smart energy-efficient buildings. The two most cited scholars had two publications each, while the top journals are the Journal of Cleaner Production and Sustainable Production and Consumption. This study concludes that there is a need for research within the construction sector to focus on CEBMs’ archetypes and frameworks. This will enable a smooth transition from linear to circular business models in the sector. Research limitations/implications The information was gathered from a single database, Scopus; hence, using other databases, including Web of Science, Google Scholar and Dimensions, might produce more articles for examination and, consequently, different findings on the subject under investigation. Practical implications These findings would assist researchers in considering the areas mentioned, which are yet to receive attention, and, by extension, enhance economic development while maintaining environmental sustainability. Originality/value This paper made a significant contribution to the body of knowledge by identifying scholars and platforms that have been instrumental in advancing CEBM research and highlighting new areas that require attention in the construction sector.

A Review of Privacy Concerns in Energy-Efficient Smart Buildings: Risks, Rights, and Regulations

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants.

Phase-Change Material Thermal Energy Storage for the Smart Retrofitting of Existing Buildings

This article presents the use of phase-change material (PCM) thermal storage within the Horizon 2020 HEART project (Holistic Energy and Architectural Retrofit Toolkit), aimed at decarbonising the European building sector through the retrofitting of existing structures into energy-efficient smart buildings. These buildings not only reduce energy consumption, but also incorporate advanced technologies for harnessing green energy, thereby promoting environmental sustainability. The HEART project employs state-of-the-art technologies for electricity production/dispatching and heat generation/storage, managed by a cloud-based platform for the real-time monitoring of parameters and optimising energy utilisation, enabling users to control their environmental comfort. The article provides a detailed examination of one of the project’s demonstration sites in Italy, focusing on various components such as heat pumps, photovoltaic systems (PV), controllers, and particularly emphasising the significance of storage tanks. The study involved the measurement and analysis of three heat storage tanks, each with a total volume of 3000 L. These tanks utilised PCM modules for latent heat storage, significantly enhancing overall heat accumulation. Water served as the heat transfer fluid within the tanks. Through meticulous calculations, the article quantifies the accumulated heat and presents a comparative evaluation between PCM-based storage tanks and conventional water tanks, showcasing the advantages of PCM technology in terms of increased heat retention and efficiency.

Smart Buildings in the Age of Internet Technology: Civil Engineering's Role in Shaping an Energy-Efficient Future

The integration of Internet Technology, particularly the Internet of Things (IoT), is radically transforming several sectors, including civil engineering and construction. This article scrutinizes the transformative capacity of IoT technology in formulating and deploying energy-efficient smart buildings. These innovative structures are designed for optimum efficiency, sustainability, and user experience. Various challenges and opportunities emerging within this rapidly growing domain are examined, and the future direction of smart building technology is anticipated, taking into account recent progress and innovative research. Within the context of contemporary civil engineering, this detailed analysis highlights the most recent advancements, providing valuable insights for other researchers in the field. This article contributes to the ongoing dialogue about the role of IoT in civil engineering and its potential to foster an energy-efficient future in smart building design and implementation.

Analyzing and Managing Various Energy-Related Environmental Factors for Providing Personalized IoT Services for Smart Buildings in Smart Environment

More energy is consumed by domestic appliances all over the world. By reducing energy consumption, sustainability can be improved in domestic contexts. Several earlier approaches to this problem have provided a conceptual overview of green and smart buildings. This paper aims to provide a better solution for reducing energy consumption by identifying the fields of abnormal energy consumption. It creates a better environment-friendly smart building to adopt the various lifestyles of people. This paper’s main objective is to monitor and control the energy efficiency of smart buildings by integrating IoT sensors. This paper mainly analyzes various prime factors that can help to improve energy efficiency in smart buildings. Factors impacting energy consumption are analyzed, and outliers of energy consumption are predicted and optimized to save energy. Various parameters are derived from IoT devices to improve energy efficiency in lighting and HVAC controls, energy monitoring, building envelope and automation systems, and renewable energy. The parameters used in water, network convergence, and electrical and environmental monitoring are also used for improving energy efficiency. This paper uses various IoT devices for monitoring and generating data in and around a smart building and analyzes it by implementing an intelligent Information Communication Technology (ICT) model called the Dynamic Semantic Behavior Data Analysis (DSBDA) Model to analyze data concerning dynamic changes in the environment and user behavior to improve energy efficiency and provide better sustainable lifestyle-based smart buildings. From the analyzed output, the outliers of the power consumption and other abnormalities are identified and controlled manually or automatically to improve sustainability regarding energy use in smart buildings.

An Energy-Efficient Smart Building System using Autonomous Networks

A sensing system is an emerging paradigm for making smart buildings. The system's success depends on correctly communicating the sensing information from a building to the end-user within the given constraints. An autonomous network can maintain and reconfigure itself in a dynamic environment without human intervention. A sensing system using an autonomous network takes decision based on the current scenario and makes the system smarter. In this article, we propose an energy-efficient smart building system. The system uses the LoRa protocol for communicating the information. The system consists of compression-decompression and autonomous network models for reducing the size of the sensing information and automatically selects the suitable compression ratio of the sensing information that can be communicated to the operator with the given constraints. Our results demonstrate the impact of the proposed compression-decompression and autonomous network models on the system's delay, energy consumption, and accuracy.

An Opto- and Thermal-Rewrite PCM/CNF-IR 780 Energy Storage Nanopaper with Mechanical Regulated Performance.

In spite of efforts to fabricate self-assembled energy storage nanopaper with potential applications in displays, greenhouses, and sensors, few studies have investigated their multiple stimuli-sensitivities. Here, an opto- and thermal-rewrite phase change material/cellulose nanofibril (PCM/CNF) energy storage nanopaper with mechanical regulated performance is facilely fabricated, through 5min sonication of PCMs and CNFs in an aqueous system. The combination of PCM and CNF not only guarantees the recyclability of PCM without leakage, but also offers nanopaper adaptive properties by leveraging the mobility and optical variation accompanying solid-to-liquid transition of PCM. Besides, trace near-infrared (NIR) dye (IR 780) in it imparts a PCM-embedded nanopaper photothermal effect to modulate the local transparency via time- and position-controlled laser exposure, leading to a reusable opto-writing nanopaper. Furthermore, since the synergistic effect of stick-and-slip function attributes from PCMs and pore structures are produced by calcium ions, the PCM/CNF energy storage nanopaper exhibits excellent mechanically regulated performance from rigid to flexible, which greatly enriches their application in energy-efficient smart buildings and displays.

Decision Support System to Classify and Optimize the Energy Efficiency in Smart Buildings: A Data Analytics Approach.

In this paper, an intelligent data analysis method for modeling and optimizing energy efficiency in smart buildings through Data Analytics (DA) is proposed. The objective of this proposal is to provide a Decision Support System (DSS) able to support experts in quantifying and optimizing energy efficiency in smart buildings, as well as reveal insights that support the detection of anomalous behaviors in early stages. Firstly, historical data and Energy Efficiency Indicators (EEIs) of the building are analyzed to extract the knowledge from behavioral patterns of historical data of the building. Then, using this knowledge, a classification method to compare days with different features, seasons and other characteristics is proposed. The resulting clusters are further analyzed, inferring key features to predict and quantify energy efficiency on days with similar features but with potentially different behaviors. Finally, the results reveal some insights able to highlight inefficiencies and correlate anomalous behaviors with EE in the smart building. The approach proposed in this work was tested on the BlueNet building and also integrated with Eugene, a commercial EE tool for optimizing energy consumption in smart buildings.

Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review

The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery’s lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors’ batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor’s layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers’ protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor’s architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.

Secure and energy-efficient smart building architecture with emerging technology IoT

With the advent of the Internet-of-Things (IoT), it is considered to be one of the latest innovations that offer interesting opportunities for different vertical industries. One of the most relevant IoT technology areas is smart construction. IoT operates in several sectors on a daily basis; implementation includes smart building, smart grids, smart cities, smart houses, physical defense, e-health, asset, and transportation management, but it is not restricted to this. Support from smart IoT buildings is an IoT-level, connected, and cost-effective system. Commercial space has major requirements in terms of comfort, accessibility, security, and energy management. Such requirements can be served organically by IoT-based systems. As the supply of energy has been exhausted and energy demand has risen, there has been a growing focus on energy usage and the maintenance of buildings. With the use of evolving IoT technology, we present a secure and energy-efficient smart building architecture. Every device is known by its unique address, and one of the key web transfer protocols is the Constrained Application Protocol (CoAP). It is an application layer protocol that does not use protected channels for data transfer. Automatic key management, confidentiality, authentication, and data integrity are all features of the Datagram Transport Layer Protection (DTLS). To achieve energy efficiency, we propose a smart construction architecture that, through IoT, manages the performance of all technological systems. The results of the simulation show that the energy consumption is lowered by about 30.86% with the use of the CoAP in the smart building, which is less than the Message Queuing Telemetry Transport case (MQTT). This paper also aims to observe how to integrate the DTLS protocol with the Secure Hash Algorithm (SHA-256) using optimizations from the Certificate Authority (CA) to improve security.

A Unified Methodology to Predict Wi-Fi Network Usage in Smart Buildings

People usually spend several hours per day inside buildings, and they require great amounts of energy and resources to operate. Although there are numerous studies about smart buildings, there is still a need for new intelligent techniques for efficient smart building management. This paper proposes the use of Wi-Fi network association information as a basis for the design of intelligent systems for smart buildings. We propose a unified experimental methodology to evaluate machine learning (ML) models on their capacity to accurately predict Wi-Fi access point demand for energy-efficient smart buildings. The evaluation involves the use of multiple classification and regression models using a variety of configurations and algorithms. We conducted an experimental analysis using our proposed methodology to determine which ML models provide the best performance results using data collected from a large scale Wi-Fi network located at Fluminense Federal University (UFF) over a period of 6 months. The proposed methodology enables the user to evaluate and to create ML models for energy efficient smart building management systems. We achieved 86.69% accuracy for occupancy prediction using classification techniques and RMSPE (Root Mean Squared Percentage Error) of 0.29 for occupancy count prediction using regression techniques.

Context-Aware Wireless Sensor Networks for Smart Building Energy Management System

Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs.

Development of CNN-based visual recognition air conditioner for smart buildings

Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.

Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis

Solar radiation measurements are highly important for achieving energy efficiency in smart buildings as well as solar energy production. They are commonly acquired with pyranometer sensor device. However, due to its high initial and maintenance costs it is not densely deployed in the field. Consequently, it provides only limited coverage as a data source for solar radiation. Hence, theoretical, empirical and/or data-driven models are utilized to estimate solar radiation in areas without pyranometers using only data from meteorological sensor stations, which on the other hand are widely available and obtained from sustainable sensor networks. In this paper, end to end process is described for building hybrid models for solar radiation using Artificial Intelligence (AI), or more specifically Machine Learning (ML) methods, after which a detailed analysis is performed on (1) the accuracy of the models regards to their parameters and input features, (2) the sustainability of the models in the real world, and finally (3) their feasibility in (near) real-time monitoring.The results are expressed with relative root mean squared error (RRMSE) and they show that hybrid models outperform model- and data-driven ones, with artificial neural network giving the best results (RRMSE = 0.0393). Additionally, the models can be enhanced by performing an informed feature selection, where a posteriori selection proves to be better than a priori selection (RRMSE = 0.0371). Further investigation shows that randomly selected input data gives faster model convergence as expected. However, sequential input data can match it if model training starts with autumn or spring data when weather exhibits sufficient variety. When applied on different times scales, all models perform best on 3h scale rather than daily, where random forest (RRMSE = 0.0275) outperforms neural network (RRMSE = 0.0315). However, for (near) real time usage the models perform almost the same as for daily, with RRMSE of 0.0469 for 1min scale with neural network. This demonstrates the feasibility of the hybrid models in Internet of Things (IoT) applications, which commonly require at least hourly intervals for solar radiation.

Energy Efficiency in Smart Buildings: IoT Approaches

The Internet of Energy (IoE) impacts on smart cities’ power sector. IoE is an implementation of the Internet of Things technology (IoT) into distributed energy systems and aims to achieve energy efficiency, to avoid energy wasting, and improve environmental conditions. IoE technology includes, among others, utilizing smart sensors and renewable energy integration. Therefore, the IoE is becoming a legal science tool to serve the purpose of a smart city. In this paper, we refer to the reasons that led the European Union to compile Regulations for facilitating transformation of existing cities, starting from existing buildings, into smart buildings. We propose a smart building template that manages the performance of all technical systems through IoT technology with the view of achieving energy efficiency. In addition, in order to improve the certification of existing buildings, as for energy performance, we propose an automated remote, control method supported by cloud interface. This method minimizes time consuming procedures and stores, on a cloud platform the energy performance of each building, for the purpose of drawing conclusion and applying measures.

Comparing the Effect of Process Fluid for Wet Ground Heat Exchanger

Nowadays, a considerable part of the energy need is provided from non-renewable energy sources such as coal, oil and natural gas. Although fossil fuels are formed in long periods, especially these days they are consumed very rapidly. Therefore, the investigations on renewable energy sources, which can be used as an alternative to fossil fuels are increasing with each passing day. Recently, in order to increase heat transfer in the ground heat exchanger, the applications of moistening the soil are existing in the literature. In this research study, the effects of process fluid on heat transfer were investigated comparatively in a wet-soil source heat exchanger. The contribution to the thermal efficiency of air and water as a process fluid was examined. In this study, it is aimed to reduce energy costs. It has been determined that it will make a significant contribution to energy saving in energy-efficient smart buildings.

Envelop Insulation for Energy Efficient Smart Buildings in India

The building sector is amongst the largest energy consuming sector across the world. Within the building sector, about 75 percent of the total energy is consumed by the residential buildings. The energy sector is directly and indirectly associated with greenhouse gas emissions, since majority of energy sector is dependent on fossil fuels, which are primary source of emanation. Building energy requirements are comprised of need for lighting, air conditioning and other home appliances operations. Air conditioning requirement is derived from the climatic condition in which a building exists. The building components such as walls, roof and windows are exposed to solar radiation and outside air temperature. Heat is conducted from exterior environment into the interior of the building envelop through these channels, wherein majority (approx. 60%) is conducted by walls and roof. Envelop insulation is considered as an efficient retrofitting measure to reduce heat gain from walls and roof, thereby need of air conditioning to remove that heat. In this work, envelop insulation has been designed for the Indian residential buildings existing in the three climate zones of India, i.e. warm-humid, hot-dry and composite climate zones. Electricity saving potential in an insulated envelop and associated life cycle cost saving analysis is carried out considering the application of four commercial insulation materials namely, expanded polystyrene, fiberglass, polyurethane foam and extruded polystyrene. It was concluded that at the design temperature 25˚C, annual electricity saving potential in retrofitted wall per unit area varies from 9 kWh to 16 kWh, whereas in case of roof it varies from 18 kWh to 28 kWh per unit area per year. Fiberglass is observed as the most cost effective insulation material, considering life cycle performance of the insulation material for all the climate zones.

A CMOS-MEMS Thermoresistive Micro Calorimetric Flow Sensor With Temperature Compensation

In this paper, we propose a new 1D thermoresistive micro calorimetric flow (TMCF) sensor model for moist airflow with the temperature-dependent thermophysical properties. The 1D model and the corresponding proposed equivalent circuit model enable rapid design optimization of an ambient temperature-compensated TMCF (T2MCF) sensor integrated with the interface circuit by using a commercial 0.35- $\mu \text{m}$ 2P4M CMOS MEMS technology. The fabricated sensor shows a prominent sensitivity of 0.543 V/(m/s) and a low power consumption of less than 2.8 mW. Moreover, in comparison with the large drifting of 49% for the uncompensated TMCF sensor, the T2MCF sensor’s output has an ultralow drifting of 0.5% with the ambient temperature variation of 22°C~48°C. Therefore, this robust T2MCF sensor can be a promising Internet of Things (IoT) for smart energy-efficient buildings. [2018-0241]