Energy Efficient Smart Research Articles

Optimization of electrochromic Mo doping WO3 films: A study on dual-phase stacked structures for energy-efficient smart windows

Amorphous tungsten trioxide (a-WO3) films were prepared on ITO conductive glass via electrodeposition and subsequently crystallized to obtain crystalline WO3 (c-WO3) films by heating a-WO3. A dual-phase stacked WO3 film was fabricated by covering Mo-a-WO3 film onto c-WO3/ITO substrate using electrodeposition and thermal-assisted electrodeposition methods, respectively. Optimization of electrochromic performance was achieved by varying Mo doping levels (0∼5 atom%). Results demonstrate that appropriate Mo doping (3 atom%) enhances the electrochromic properties of a-WO3 films. Mo doping introduced structural distortions that reduced energy barriers and enhanced ion mobility, leading to improved electrochemical and electrochromic properties. The intermediate c-WO3 layer improves adhesion between a-WO3 top film and ITO glass substrate, while the porous structure of a-WO3 layer increases the number of active sites for electrochromic reactions. Mo3-a-WO3/c-WO3 dual-phase stacked film with doping 3 atom% Mo shows an optical modulation range of 83.4 % at 633 nm, a coloration efficiency of 74.3 cm2/C, rapid response time (bleaching/coloration: 3.4 s/6.1 s), and 86.6 % retention of its maximum current density after 2000 cycles, respectively. The high oxidation ion diffusion coefficient (3.53 × 10−10 cm2/s) and reduction diffusion coefficient (1.55 × 10−10 cm2/s) were also observed. This dual-phase stacked film shows significant improvements in electrochromic performance due to the synergistic effects between the dual phases and Mo-doping. Electrochromic device (ECD) assembled with Mo3-a-WO3/c-WO3 dual-phase films as the working electrode, ITO glass as the counter electrode, and 1 mol/L LiClO4/PC solution as the electrolyte exhibited an optical modulation range of 74.2 % and response time (bleaching/ coloring) of 6.8 s/3.7 s. These findings confirm that ECD with Mo-a-WO3/c-WO3 dual-phase films offer excellent electrochromic performance.

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.

An ML-Based Solution in the Transformation towards a Sustainable Smart City

The rapid development of modern information technology (IT), power supply, communication and traffic information systems and so on is resulting in progress in the area of distributed and energy-efficient (if possible, powered by renewable energy sources) smart grid components securely connected to entire smart city management systems. This enables a wide range of applications such as distributed energy management, system health forecasting and cybersecurity based on huge volumes of data that automate and improve the performance of the smart grid, but also require analysis, inference and prediction using artificial intelligence. Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. This article presents and develops the authors’ own concept in this area, which is planned for research in the coming years. The paper aims to develop and initially test a conceptual framework that takes into account the aspects discussed above, emphasising the practical aspects and use cases of the Social Internet of Things (SIoT) and artificial intelligence (AI) in the everyday lives of smart sustainable city (SSC) residents. We present an approach consisting of seven algorithms for the integration of large data sets for machine learning processing to be applied in optimisation in the context of smart cities.

Advanced smart HVAC system utilizing borehole thermal energy storage: Detailed analysis of a Uppsala case study focused on the deep green cooling innovation

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

Energy efficiency in smart schools using renewable energy strategy

As smart schools increasingly rely on technology, achieving energy efficiency becomes crucial for cost reduction and sustainability. This study investigates energy efficiency strategies in smart schools, focusing on the integration of renewable energy technologies. A quantitative approach using numerical simulations and literature reviews establishes benchmarks for energy-efficient smart schools. The Design Builder software is employed to evaluate system performance, with validation achieved through analysis in the System Advisor Model software. The modeled smart school building in Design Builder consumes 75,385.63 kWh annually, based on the weather conditions of specific location. Further studies indicate that integrating photovoltaics and hot water collectors can generate approximately 86,635 kWh annually. This not only offsets the energy consumption of building but also produces an excess of 11,249 kWh, which can be transferred back to the grid for additional revenue. Validation using SAM software demonstrated a minimal difference (3.2%) in annual energy outputs, confirming the accuracy of the model. The findings suggest that photovoltaics and hot water collectors can significantly contribute to achieving net-zero energy consumption in smart school buildings. Additionally, a focus on rooftop installations promotes sustainability by minimizing land use.

New graphene oxide doped polymer dispersed liquid crystal nanocomposites targeted to eco-friendly and energy-efficient smart windows

Graphene oxide-polymer dispersed liquid crystal (GO-PDLC) composites are proposed as a solution for energy-efficient switchable windows. These composites were prepared by incorporating varying concentrations of graphene oxide (GO) into a mixture of nematic liquid crystal (E7) and a UV–Vis curable resin using the polymerization-induced phase separation technique. The results indicate that the dispersion of GO at an optimum concentration of 0.005 wt% significantly affects the morphology, phase transition temperatures, and electro-optic properties of polymer dispersed liquid crystal (PDLC). In particular, the GO-PDLC composites exhibit a transition from an opaque to a transparent state at a remarkably low voltage (∼11.5 V) compared to pure PDLC cell. Dielectric analysis reveals that adding GO increases the permittivity, conductivity, and dielectric strength of PDLC composites. Moreover, the Cole-Cole plot indicates a non-Debye type relaxation in a higher frequency region. Furthermore, a blue shift in emission wavelength and enhanced photoluminescence intensity suggest alterations in electronic transitions within the composites. This comprehensive study provides valuable insights into optimizing the properties of PDLCs through the dispersion of GO, thereby offering promising prospects for their utilization in various optoelectronic applications.

Energy-Efficient Smart Window Based on a Thermochromic Hydrogel with Adjustable Critical Response Temperature and High Solar Modulation Ability.

Thermochromic smart windows realize an intelligent response to changes in environmental temperature through reversible physical phase transitions. They complete a real-time adjustment of solar transmittance, create a livable indoor temperature for humans, and reduce the energy consumption of buildings. Nevertheless, conventional materials that are used to prepare thermochromic smart windows face challenges, including fixed transition temperatures, limited solar modulation capabilities, and inadequate mechanical properties. In this study, a novel thermochromic hydrogel was synthesized from 2-hydroxy-3-butoxypropyl hydroxyethyl celluloses (HBPEC) and poly(N-isopropylacrylamide) (PNIPAM) by using a simple one-step low-temperature polymerization method. The HBPEC/PNIPAM hydrogel demonstrates a wide response temperature (24.1-33.2 °C), high light transmittance (Tlum = 87.5%), excellent solar modulation (ΔTsol = 71.2%), and robust mechanical properties. HBPEC is a functional material that can be used to adjust the lower critical solution temperature (LCST) of the smart window over a wide range by changing the degree of substitution (DS) of the butoxy group in its structure. In addition, the use of HBPEC effectively improves the light transmittance and mechanical properties of the hydrogels. After 100 heating and cooling cycles, the hydrogel still has excellent stability. Furthermore, indoor simulation experiments show that HBPEC/PNIPAM hydrogel smart windows have better indoor temperature regulation capabilities than traditional windows, making these smart windows potential candidates for energy-saving building materials.

A message queuing telemetry transport (MQTT) protocol based energy-efficient smart, wireless LED street lighting solution

A message queuing telemetry transport (MQTT) protocol based energy-efficient smart, wireless LED street lighting solution

Self-powered electrochromic smart window helps net-zero energy buildings

Net-zero energy buildings are becoming an important energy-saving and low-carbon technology for global environmental protection and resource conservation. As the least energy-efficient building components, windows have promoted the development of electrochemically light/thermal management, where smart windows without external electrical supplies are highly desired for net-zero energy buildings. Here, we develop a self-powered electrochromic smart window system to regulate solar radiation transmittance, constructed by a triboelectric nanogenerator (TENG) and a high-performance electrochromic device. The visible–near-infrared (VIS-NIR) dual-band electrochromic device is well-designed based on an eco-friendly low-energy demand phenothiazine redox ionic liquid, demonstrating a fast switching response of ﹤1.9 s, a large transmittance modulation of >51.8 %, and outstanding electrochemical durability (5.5 % attenuation after 4000 cycles). Upon continuous pressing/releasing motions, the integrated TENG-powered electrochromic smart window (TECSW) displays a prompt light/thermal management behavior with desirable privacy preservation performance (transmittance modulation of 51 % in the visible region) and excellent thermal management property (12.3 °C reducti°n). The whole-building energy simulations show that TECSW can save 347.79 MJ/m2yr (amounting to 96.60 kWh/m2yr) of operational Heating Ventilation Air Conditioning (HVAC) warming/cooling energy consumption among 34 cities in China, accounting for 17.25 % of the total energy consumption. This research provides new insight into designing green and energy-efficient smart windows for net-zero energy buildings.

A flexible reverse-mode liquid crystal/polymer composite film with low driving voltage and high stability for energy-efficient smart windows

ABSTRACT Smart windows are attractive considering their contribution to energy efficiency and privacy protection. However, the achievement of flexible reverse-mode film with good stability and well electro-optical performance for smart windows remains a fascinating challenge in recent years. Herein, we reported a low-driving-voltage, high-stability flexible reverse-mode liquid crystal/polymer composite film for energy-efficient smart windows by the formation of polymer structure with a polyhedral oligomeric silsesquioxane (POSS) core and strong epoxy resin as chains. The experimental results suggested that the polymer can not only stabilise the planar state of the cholesteric liquid crystal in the zero field but also provide enough support to the continuous liquid crystal phase on flexible substrate. Besides, the optimised optical device could be fully driven at 30 V (lower than safety voltage, 36 V) and keep contrast ratio of 100. Moreover, the flexible film showed outstanding optical regulation performance and excellent cycle stability under electric field. The strategy used in this research would open new doors for the development of energy-efficient smart windows in buildings.

Energy Aware Smart Car Parking System Based Internet of Spatial Things

With the advent of smart and green cities, the development of energy-efficient smart parking systems has attracted researchers to reduce environmental pollution in smart cities due to reducing traffic congestion as well as waste of time and fuel consumption. This article investigates how to integrate the Internet of Spatial Things (IoST) with workload balancing and image processing in fog computing to build an energy-efficient smart parking system. The suggested system applies Q-Learning, a reinforcement learning method, to achieve workload balancing and then deploys image processing to detect vacant parking slots. The proposed method’s main objective of load balancing is to reduce energy consumption. The evaluation of the proposed system is done by comparing two case studies, fog-based and cloud-based IoST implementations, in the iFogSim simulator for various scenarios and scales. Moreover, it evaluates and compares the energy consumption of various devices. Experimental findings show that the devised system in fog-based IoST greatly reduces energy consumption with improves parking space availability in smart parking in contrast to the cloud-based deployment of smart car parking.

GSM Based Smart Irrigation System with Micro Controller Powered by Solar Panel

In recent years, the smart irrigation industry is forecasted to elevate the market value to USD 1,404.4 million, with an anticipated annual compound growth rate of 13.84%. This surge is credited to the significant enhancements in Research and Development, which have catalyzed the advancement and expansion of energy-efficient smart irrigation technologies. Such advancements are propelling the rapid development within the agricultural tech sector. Moreover, the prevalence of extreme climatic conditions globally necessitates highly efficient irrigation systems to adapt to such environmental challenges. India, predominantly an agrarian society, relies heavily on its farming community. The pivotal role of farmers is undeniable in contributing to the nation’s agricultural output. Agriculture remains a fundamental aspect of sustenance for a vast majority of the Indian population, playing a vital role in the economic stability of the country.

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

A Multistate Thermoresponsive Smart Window Based on a Multifunctional Luminescent Solar Concentrator.

Conventional luminescent solar concentrators (LSCs) usually only have the ability to absorb solar energy and convert it to electricity but are not able to regulate the transmitted light. Herein, a multistate thermoresponsive smart window (SW) based on LSC has been fabricated, in which the stimuli-responsive host layer consists of polydimethylsiloxane (PDMS) and ethylene glycol solution (EGS) microdroplets stacking with LSC layer-based on near-infrared (NIR) CuInSe2-xSx/ZnS core/shell quantum dots (QDs) and PDMS matrix. As-synthesized CISSe/ZnS QDs with broad NIR absorption in LSC exhibit controllable emission spectra over 833-1088 nm and high photoluminescence (PL) quantum yield from 45 to 83%. Coupling with Si solar cells as a reference, optimized LSC-SW devices with dimensions of 5 × 5 × 0.9 cm3 exhibit higher power conversion efficiency (PCE) of 1.19-1.36% with increased temperature from 0 to 50 °C than those of sole LSC and SW devices. The corresponding visible light transmissions are regulated from 75.1 to 48.1% accordingly. The improvement of PCEs in an opaque state is mainly due to enhanced absorption of QDs originating from rescattered photons from the EGS/PDMS layer, leading to more emitted photons reaching photovoltaics. This work is expected to bring up new opportunities for applications in greenhouses, building facades, and energy-efficient smart windows.

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.

Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes

Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes

Recent Advance in Electrochromic Materials and Devices for Display Applications.

Electrochromic devices (ECDs) possess the performance advantages in terms of color adjustability, low power consumption, and visual friendliness, emerging as one of the ideal candidates for energy-efficient smart windows, next-generation displays, and wearable electronics. The optical and electrical characteristics of ECDs can be adjusted by modifying the materials or structure of devices. This review summarizes the recent developments of innovative technologies and key materials of ECDs for display applications, highlighting the key issues and development trends in this area.

Power PV Forecasting using Machine Learning Algorithms Based on Weather Data in Semi-Arid Climate

As the energy demand continues to rise, renewable energy sources such as photovoltaic (PV) systems are becoming increasingly popular. PV systems convert solar radiation into electricity, making them an attractive option for reducing reliance on traditional electricity sources and decreasing carbon emissions. To optimize the usage of PV systems, intelligent forecasting algorithms are essential. They enable better decisionmaking regarding cost and energy efficiency, reliability, power optimization, and economic smart grid operations. Machine learning algorithms have proven to be effective in estimating the power of PV systems, improving accuracy by allowing models to understand complex relationships between parameters and evaluate the output power performance of photovoltaic cells. This work presents a study on the use of machine learning algorithms Catboost, LightGBM, XGboost and Random Forest to improve prediction. The study results indicate that using machine learning algorithms LightGBM can improve the accuracy of PV power prediction, which can have significant implications for optimizing energy usage. In addition to reducing uncertainty, machine learning algorithms improve PV systems’ efficiency, reliability, and economic viability, making them more attractive as renewable energy sources.

Thermochromic hydrogel-based energy efficient smart windows: fabrication, mechanisms, and advancements.

Thermochromic smart windows are regarded as highly cost-effective and easily implementable strategies with zero energy input among the smart window technologies. They possess the capability to spontaneously adjust between transparent and opaque states according to the ambient temperatures, which is essential for energy-efficient buildings. Recently, thermochromic smart windows based on hydrogels with various chromic mechanisms have emerged to meet the increasing demand for energy-saving smart windows. This review provides an overview of recent advancements in hydrogel-based thermochromic smart windows, focusing on fabrication strategies, chromic mechanisms, and improvements in responsiveness, stability and energy-saving performance. Key developments include dual-responsiveness, tunable critical transition temperatures, freezing resistance, and integrations with radiative cooling/power generation technologies. Finally, we also offer a perspective on the future development of thermochromic smart windows utilizing hydrogels. We hope that this review will enhance the understanding of the chromic mechanism of thermochromic hydrogels, and bring new insights and inspirations on the further design and development of thermochromic hydrogels and derived smart windows.

Complementary electrochromic device via a scalable solution process: a step towards affordable and energy-efficient smart windows

A scalable solution process method is demonstrated to achieve energy-efficient and affordable smart windows.