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English Heritage (EH) is a large cultural heritage organisation, which cares for 1million artefacts over 136 sites. Preventive conservators and conservation scientists manage a mixture of controlled and uncontrolled historic site environments housing diverse object types. Two fine art conservators manage treatment programmes for easel and wall paintings, gilded frames and gilded furniture. Other objects are managed by preventive conservators and outsourced to private conservation studios for treatment. Sustainability is becoming an increasingly important part of the conservation team's agenda. Measuring and understanding the carbon impact of both the preventive and interventive conservators' work was an important starting point for creating more sustainable solutions. To create a baseline for future monitoring and carbon reduction, carbon footprint studies were undertaken to evaluate the carbon impact of interventive painting conservation treatments; preventive conservation framing (adapting frames and making microclimates for displaying paintings in non-ideal environments); and running conservation (humidistatic) heating vs air conditioning systems in display rooms. [ABSTRACT FROM AUTHOR]

The energy consumption of residential buildings plays a crucial role in overall energy consumption and environmental sustainability. This paper aims to conduct an energy analysis of a residential house located in China, with a focus on comparing the accuracy of the model, identifying areas for improvement, and proposing energy-efficient solutions. Four sets of temperature sensors were placed to monitor the ambient temperature at which the building is located and the indoor temperature of the residential building during a heating season. The energy consumption of keeping the building running at a low temperature was recorded and compared with the simulation results to verify the accuracy of the model. The monitoring results give the weekly average temperature of each zone on each floor, and the door and window positions, room layouts, and orientations are discussed to analyze the thermal response of the building. In addition, the effect of the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the roof, and the solar heat gain coefficient (SHGC) of the exterior windows on the heating energy consumption of the building are further analyzed through simulations. The results show that, after adding a certain thickness of insulation to the exterior walls and roofs of a building, increasing the thickness of the insulation layer produces little extra energy saving. The use of building windows with high SHGC can effectively reduce building heating energy consumption. [ABSTRACT FROM AUTHOR]

Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric-a technique recommended by a technical advisory group-was used to measure the NILMs success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM products estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications.

The present paper successfully employs a modified sol-gel method to synthesize TiO 2 –SiO 2 nanocomposites. By incorporating a hygrometer and energy monitoring devices, we achieved real-time monitoring of the drying process and optimized temperature control. Energy consumption during the drying process was observed, revealing that higher temperatures result in shorter drying times. The lowest energy consumption was recorded at 80 °C, while the optimal temperature, obtained from the conversion of the optimization diagram, occurred at 77 °C. Characterization was performed using different tools such as XRD, FT-IR analysis, XPS, FE-SEM-EDS, and HRTEM to determine the size and distribution of elements, crystal structure, and chemical bonds. Furthermore, an anti-bacterial test was conducted, revealing that the nanocomposite exhibited superior antibacterial properties against Staphylococcus aureus and Escherichia coli. Meeting the required standards, the TiO 2 –SiO 2 nanocomposite produced by this method shows the potential to create a cleaner and more efficient environment in medical and industrial applications. [ABSTRACT FROM AUTHOR]

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform. [ABSTRACT FROM AUTHOR]

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This study takes an old oceanarium in Jiangsu Province, China, as a case study and monitors the energy consumption values related to electricity, water and gas consumption for the whole year before the renovation of the oceanarium. Based on the energy consumption monitoring data, an in-depth analysis of the energy consumption defects of the oceanarium before the renovation is conducted using energy audit technology. We combine the characteristics of the oceanarium itself according to: (1) the results of the building energy audit; (2) the results of the envelope calculation; and (3) the latest energy-saving retrofit technology. We developed a more scientific building energy-saving retrofit programme. Using this method, we can accurately monitor the energy consumption problems in old oceanarium buildings and find building energy consumption defects. In this study, we monitor the real-time energy consumption of nine old government office buildings in Jiangsu Province, China using energy monitoring technology. We analyse and study the monitoring results and finally target the scientific energy-saving retrofits. This method can be used to carry out targeted energy-saving building renovation in the future and put forward new methods and ideas for studying the energy consumption of existing public buildings.

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering energy bills, and extending the life of these historic landmarks all depend on fixing the energy inefficiency that plagues these buildings. Despite advanced technologies' remarkable progress, the potential of the Internet of Things and deep learning in older buildings has not been unexplored. Major obstacles include expensive and out-of-date infrastructure and the difficulty of incorporating new technology into historically significant structures. Existing research has mostly ignored older infrastructures' unique requirements and limitations in favour of current or newly built services. In addition, comprehensive research on integrating deep learning with the Internet of Things in this specific environment has been lacking. Smart building energy management is made possible by the Internet of Things (IoT) and deep learning. Architectural limitations, outmoded infrastructure, and the necessity for non-invasive retrofitting solutions all contribute to the difficulty of energy monitoring and improvement in older buildings. This research proposes combining the IoT with Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) to make an energy management system that can change and adapt to the needs of older buildings. Data from IoT sensors is collected on occupancy, temperature, lighting, and equipment usage and then analyzed using Deep Learning models to determine the most efficient energy consumption patterns. Beyond its energy-saving potential, this method has many potential uses. Spotting and fixing structural problems before they become major can improve occupant comfort, reduce maintenance costs, and pave the way for predictive maintenance. Integration with the Smart grid and demand response programs can be facilitated, too, improving the reliability of the power system as a whole. Our IoT and Deep Learning-based energy management solution optimizes energy usage, reduces expenses, and mitigates environmental impact in older buildings, as shown by extensive simulation studies. The system's performance is compared to more conventional methods, and its flexibility is evaluated in various building and usage contexts. The experimental outcomes show the suggested DL-PEM model increases the energy demand forecasting analysis, energy consumption analysis, thermal comfort optimization analysis, seasonal variation analysis, and occupancy data analysis.

Gaza's electrical deficit has resulted in the rise of new energy suppliers that sell power from private generators at higher prices. Consumers have challenges because they cannot tell whether their energy originates from the utility or private generators, resulting in unexpectedly large bills owing to a lack of an electronic monitoring system. This study fills this gap by developing a low-cost IoT energy monitoring system that provides real-time information on energy use (hourly, daily, and monthly) to assist consumers in managing their usage and expenditures. An ESP32 microcontroller is used in the system to gather data from energy meters, analyze it, and deliver updates to users via WhatsApp over a secure Wi-Fi connection via the Blynk platform. Experimental findings show that the system accurately captures voltage, current, active power, and cumulative power consumption and transmits this information to customers in real time.

Abstract The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

Introduction. The ideology of Fast Track-Surgery (FTS) and Enhanced Recovery After Surgery (ERAS) represents modern trends in global surgical practice. Anesthesiology aims to provide ideal and universal solutions for protecting patients from surgical aggression. A multimodal approach, which includes multilevel, multifaceted antinociception, is well-founded, where maximum effectiveness is combined with minimal side effects. The aim. To analyze the issue of multimodal pain management as an important component of ERAS (Enhanced Recovery After Surgery) from modern sources of literature for optimizing perioperative care in the minimally invasive surgery department. Materials and methods. Bibliosemantic, comparative, and systemic analysis methods were employed. The proposed recommendations were developed based on an analysis of modern literature, the results of randomized trials and meta-analyses, as well as our own studies dedicated to the study of perioperative pain issues. The results. It has been proven that the use of opioids in anesthesia practice is decreasing. New and modern methods of continuous monitoring of vital organs in patients are being introduced as supplements to classical monitoring protocols. These methods are used to avoid excessive or inadequate dosing of anesthetics, adjust the concentration of substances used, reduce post-anesthesia complications, and improve patient comfort. The method of perioperative energy monitoring significantly complements the "International Standards for Safe Anesthetic Practice", WFSA (2010), and enhances perioperative patient safety by detecting metabolic disturbances and implementing appropriate pathogenetic correction. The importance of regional anesthesia under ultrasound control, nociception-antinociception balance management, and the impact of these methods on opioid consumption, patient satisfaction, and postoperative recovery have been proven. Conclusions. The most commonly used combination of drugs for multimodal analgesia often includes acetaminophen and NSAIDs (strong recommendation). It has been proven that deep and prolonged sedation in intensive care unit (ICU) patients is associated with worse outcomes, longer mechanical ventilation duration, extended ICU and hospital stays, and higher rates of complications, including infections and sometimes even death.

Determining the environmental impact of asphalt mixtures is crucial for sustainable infrastructure development. A dominant factor in the ecological performance of the building material is the heating process in the dryer drum of an asphalt mix plant. Since it depends on various parameters, large scattering can be observed. While most Product Category Rules (PCRs) only consider the mean consumption over a whole production year in Module A3 according EN 15804, and consequently assign the same value to each ton asphalt mixture produced, the advantages of some asphalt mix designs remain unconsidered. Based on primary data of a batch asphalt mix plant, this case study investigated the impact of production amount, asphalt mixture temperature, number of dryer drum starts and the addition of reclaimed asphalt pavement (RAP) on the energy consumption of asphalt mixture production. By applying Linear Discriminant Analysis (LDA), it was demonstrated that it is not possible to precisely determine the energy consumption in the dryer drum based on the available data. Thus, further parameters are necessary for a fully-fledged prediction model. Nevertheless, the finding of this study showed the significant impact of the daily production volume, which results in 22 % less fuel consumption for high production volumes compared to low volumes. When the production temperature is lowered from 186 °C to 162 °C, further fuel savings of up to 9 % can be achieved. Considering the Global Warming Potential (GWP), the highest saving potential (27 %) in the drying and heating process can be achieved by operating the dryer exclusively with natural gas instead of heating oil.

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training.

Vending machines are a convenient source of food and drinks, but they can also be a significant source of energy consumption in public buildings. To reduce energy waste and improve energy efficiency, a low-cost sensor network was used to monitor the energy usage of vending machines. The sensor network consists of small, wireless sensors that are placed inside the vending machines, and a central hub that is connected to the internet via Wi-Fi. The sensors are able to collect data on the energy usage of the vending machines, including when they are idle, in use, or in a standby mode. This data is then transmitted to the central hub, where it is processed and made available to users through a web-based interface or mobile app. With this information, users can identify patterns in energy usage and make changes to reduce overall energy consumption. In a university in southern Spain, 53 vending machines of three types were monitored: snack, cold drinks, and hot drinks. The average monthly consumptions of these types of vending machines were snack (250 kWh), cold drinks (200 kWh), and hot drinks (100 kWh). By using a sensor network to monitor the energy usage of vending machines, it is possible to identify areas where energy is being wasted and make adjustments to reduce this waste. This can help to reduce the environmental impact of the vending machines, by reducing the amount of energy that is consumed and the associated greenhouse gas emissions. Additionally, by making the vending machines more energy efficient, it is possible to improve their overall productivity and cost-effectiveness. Overall, the use of a low-cost sensor network to monitor vending machines energy usage can be seen as a form of cleaner production, because it promotes the use of more sustainable and efficient industrial processes.

The energy consumption of residential buildings plays a crucial role in overall energy consumption and environmental sustainability. This paper aims to conduct an energy analysis of a residential house located in China, with a focus on comparing the accuracy of the model, identifying areas for improvement, and proposing energy-efficient solutions. Four sets of temperature sensors were placed to monitor the ambient temperature at which the building is located and the indoor temperature of the residential building during a heating season. The energy consumption of keeping the building running at a low temperature was recorded and compared with the simulation results to verify the accuracy of the model. The monitoring results give the weekly average temperature of each zone on each floor, and the door and window positions, room layouts, and orientations are discussed to analyze the thermal response of the building. In addition, the effect of the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the roof, and the solar heat gain coefficient (SHGC) of the exterior windows on the heating energy consumption of the building are further analyzed through simulations. The results show that, after adding a certain thickness of insulation to the exterior walls and roofs of a building, increasing the thickness of the insulation layer produces little extra energy saving. The use of building windows with high SHGC can effectively reduce building heating energy consumption.

CNC machining is a common manufacturing process which requires significant energy consumption. The first step towards energy savings is to monitor the energy consumption of CNC machines, which are usually equipped with a three-phase electrical power supply. However, existing energy metres are costly for detailed measurements of a machine's energy consumption level. This study developed a low-cost energy measurement device based on the Arduino microcontroller-based platform to monitor energy consumption during CNC machining processes. The product development methodology includes the measurement procedure, calibration, pilot study prior to the commencement of the actual study, and results validation. A field experiment was conducted to validate the design's functionality. Preliminary energy measurements were performed on tool standby, tool changing, spindle rotation speeds, and feed rates. Results showed that the average standby power of the CNC machine is 5.15 kWh, with actual power consumption for the coolant pump motor, spindle motor, and feed motor being 0.43 kW, 0.77 kW, and 0.45 kW, respectively. Energy consumption is increased with spindle rotation speed and feed rate increments. The analysis results demonstrated the product's ability as a cost-effective solution for machining energy monitoring. [ABSTRACT FROM AUTHOR]

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca's microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system's parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid's energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved. [ABSTRACT FROM AUTHOR]

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training. [ABSTRACT FROM AUTHOR]

The growing adoption of renewable energy sources and the need for more efficient and secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an issue of utmost importance, as current traditional energy meters have several problems in terms of lack of transparency, very high operational costs, and the possibility of being easily tampered with. This paper proposes a new system for electricity production metering that leverages blockchain and IoT for decentralized and secure data recording while protecting user privacy and reducing operational costs. The architecture results in improvements over the traditional energy meter. The system also contributes to the generation of big data that is reliable, traceable, error-proof, and highly resistant to cyber attacks. The architectural project outputs are a smart energy meter, a smart contract on the Ethereum blockchain, and a decentralized application to manage the information recording. The experimental prototype outcomes confirm the use of these new technologies to improve energy metering, enhancing efficiency, transparency, and traceability, with reduced costs and increased user privacy. [ABSTRACT FROM AUTHOR]

Monitoring of energy enables easy access and provides information on power use, normal and abnormal circumstances. Many applications today, mainly in the industrial sector, struggle with power quality issues. The power factor is crucial when it comes to power quality. Increased energy efficiency and lower energy expenses can be accomplished with the aid of power factor (PF) modification and monitoring of the consumption of energy. A capacitance is supplemental to counteract power factor deterioration and lessen power loss. In order to monitor a system's energy usage and automatically boost its power factor, this study attempts to develop an Automatic Power Factor Correction (APFC) system and energy monitoring using IoT techniques and develop a mobile application for increased comfort and convenience. An open-source energy monitoring library was designed and implemented for precise power calculations. Using a capacitor bank and Internet of Things (IoT) technologies, this paper conducted hardware experiments for energy monitoring and automated power factor correction concerning various loads and various real-time fluctuating case study-based assessments. The Pure restive load (R Load), series resistive-inductive loads (S-RL Load) and parallel resistive-inductive (P-RL Load) loads were used to validate the performance of the designed hardware model. The outcome demonstrates that the intended Raspberry Pi-based energy monitoring and self-governing power factor correction system excels in enhancing the power quality automatically obligating no human input through appropriate transitioning of the capacitor bank's capacitors. The inductance in this study results in a lag with a power factor of 0.747 and 0761 for the series RL and parallel RL loads, respectively. The recommended strategy connects the capacitor banks according to the inductive load, resulting in a power factor of 0.972 and 0.977 for series RL and parallel RL loads, respectively. The concerns relating to power loss, penalty, and power quality were thus handled using the suggested methodology. The design that has been suggested helps to improve the power system and is small, straightforward, and simple to execute. [ABSTRACT FROM AUTHOR]

The dataset provides all necessary variables for data-driven energy modelling of an office room. The measurement data have been obtained from an office building operating as living lab in a temperate climate of Central Europe. The temperatures and the ventilation air flowrate are raw measurements, while the heat flows are calculated from measurements. The incoming solar irradiance is calculated with two façade models –simple and enhanced–, using measurements (solar irradiance, movable shading settings) and building characteristics (geometry, glazing and shading properties). One year and four months of data is provided with a fine one-minute time step and a coarser fifteen-minute time step. The dataset can be used to test and validate data-driven models, for example for predictive control applications.

Electrical load forecasting is now a key component of planning and running a smart grid due to the ongoing integration of green energy sources (RES) into the energy mix and the transformation of the traditional electric grid into a more intelligent, flexible, and interactive system. Either when it comes to the distribution system or a single household, predicting the electric load is a difficult task due to its high volatility and uncertainty. In this paper, the study suggests a method for monitoring electrical energy that is based on long short-term memory (LSTM). The research for the suggested method is based on real data of electricity use from four distinct clients during a week. The analysis is made more effective and produces better findings owing to the use of a customized meter that transmits data to the server every minute. Modeling irregular trends in electric power consumption is made possible by the LSTM's aptitude for examining recurrent patterns in time series components. The suggested method calculates the dataset on individual household power usage with the lowest root mean square error (RMSE) as compared to existing forecasting methods. The simulation results highlight the parameters that have the biggest impact on forecasting power consumption.The suggested LSTM model substantially reduced time series forecasting errors when compared to previous techniques.

The modern world's increasing reliance on automated systems for everyday tasks has resulted in a corresponding rise in power consumption. The demand is further augmented by increased sales of electric vehicles, smart cities, smart transportation, etc. This growing dependence underscores the critical necessity for a robust smart energy measurement and management system to ensure a continuous and efficient power supply. However, implementing such a system presents a set of challenges, particularly concerning the transparency, security, and trustworthiness of data storage and retrieval. Blockchain technology offers an innovative solution in the form of a distributed ledger, which guarantees secure and transparent transaction storage and retrieval. This research introduces a blockchain-based system, utilising Hyperledger Fabric and smart contracts, designed for the secure storage and retrieval of consumers' energy consumption data. Finally, a user-friendly web portal was designed and developed using the node.js framework, offering an accessible and intuitive interface to monitor and manage energy consumption effectively. [ABSTRACT FROM AUTHOR]

Eco-conferences like COP26 in Glasgow (UK) in 2021 have brought the debate on energy consumption and climate change to the fore. Given that a third of the energy produced worldwide is consumed in the home, it is pertinent to investigate how households use emerging technologies that allow households to monitor their energy consumption. This paper investigates how Danish households use green IT to monitor and manage their energy use and studies the related meaning householders attach to the green IT. We present qualitative data collected through interviews with 14 households, electric car owners mostly, who have adopted an application to monitor green energy availability – and its derived consumption. The paper highlights these householders' green energy monitoring practices with an emphasis on the meaning they make of the green IT application they used. Our study found that households can use more green energy without interacting continuously with the green IT application. This contrasts with a common assumption in the field of green IT design that consumers must continuously engage with the green IT to consume more green energy. We also posit that including householders in future green IT design is paramount for designing successful green IT applications. Finally, this paper calls for household energy consumption studies to view energy consumption as a service where specific practices are matched to energy sources – rather than viewing energy availability as a solitary incident. [ABSTRACT FROM AUTHOR]

This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building management solutions, HomeAssistant. The System uses communication based on the Message Queue Telemetry Transport (MQTT) protocol. The example machine learning function described in this publication detects anomalies in the electricity waveforms and raises the alarm. This information determines power quality and detects system faults or unusual power consumption. Recently, increasing electricity prices on global markets have meant that buildings must significantly reduce consumption. Therefore, a fundamental element of energy consumption diagnostics requires detecting unusual forms of energy consumption to optimise the use of individual devices in home and office installations. [ABSTRACT FROM AUTHOR]

As a rapidly evolving paradigm, digital transformation (DT) remains one of today's most significant challenges. Consequently, numerous businesses devote a substantial portion of their resources to this captivating transition. However, this investment's return is difficult to quantify or even unexplored by others. This observation constituted the foundation for Solow's productivity paradox. Even if they are aware of how the KPI measurement affects their performance, managers continue to disregard this evaluation. In terms of academic literature, this field of study is still inadequately developed. To address this limitation, this study uses the NVivo software to conduct a systematic literature review highlighting the KPIs, the methodology used in the corpus, the evolution of related works, and their type. According to our analysis, several works cite this term without describing it or defining a general approach to DT metrics. In addition, the financial and commercial performance KPIs are the most specific. Following a summary of the primary KPIs in the literature, we classified them into two prominent families: generic and specific. This classification represents the originality of this paper. The generic KPIs are multidimensional and applicable to a variety of businesses, while the specific ones pertain to a particular function or industry. [ABSTRACT FROM AUTHOR]

High repetition rate lidar is typically equipped with a low-energy, high repetition rate laser, and small aperture telescopes. Therefore, it is small, compact, low-cost, and can be networked for observation. However, its data acquisition and control functions are generally not specially designed, and the data acquisition, storage, and control programs need to be implemented on an IPC (Industrial Personal Computer), which increases the complexity and instability of the lidar system. Therefore, this paper designs an integrated off-line echo signal acquisition system (IOESAS) for lidar developed based on SoC FPGA (System-On-Chip Field Programmable Gate Array). Using a hardware–software co-design approach, the system is implemented in a heterogeneous multi-core chip ZYNQ-7020 (integrated FPGA and ARM). The FPGA implements dual-channel echo data acquisition (gated counting and hardware accumulation). At the same time, the ARM performs laser control and monitoring, laser pointing control, pulse energy monitoring, data storage, and wireless transmission. Offline data acquisition and control software was developed based on LabVIEW, which can remotely control the status of the lidar and download the echo data stored in IOESAS. To verify the performance of the data acquisition system, IOESAS was compared with the photon counting card P7882 and MCS-PCI, respectively. The test results show that they are in good agreement; the linear correlation coefficients were 0.99967 and 0.99884, respectively. IOESAS was installed on lidar outdoors for continuous detection, and the system was able to work independently and stably in different weather conditions, and control functions were tested normally. The gating delay and gating width time jitter error are ±5 ns and ±2 ns, respectively. The IOESAS is now used in several small lidars for networked observations. [ABSTRACT FROM AUTHOR]

A measurement system is proposed for energy monitoring of cutting in multipurpose machines, taking account of the dynamic models of its drives. Modeling confirms that tool wear may be estimated from the power of the shaping drives in milling. Identification of the wear among the many factors increasing the power consumption entails identifying its characteristics in different operating conditions and comparing the power consumed by all the drives responsible for shaping motions. [ABSTRACT FROM AUTHOR]

In this paper, we present the final results from the research project "Urban Abacus of Building Energy Performances (Abaco Urbano Energeticodegli Edifci–AUREE)" aimed at supporting the renovation process and energy efficiency enhancement of urban building stocks. The crux of the AUREE project is a Web–GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on an urban built environment and promoting the participation of local stakeholders in its improvement. As the latest development of this research, a workflow that integrates the AUREE portal with BIM authoring and an open-source IoT platform is implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the Sotacarbo Sustainable Energy Research Center was selected as the case study. The presented results proved that it was possible to create a valid open system, which was accessible to both specialist and unskilled users, and aimed at guiding, through a progressive knowledge deepening, common end-users toward proper conscious "energy behaviors" as well as public administrations and decision-makers toward sustainable facility management. Later, the proposed open system could also be suitable to be used as an effective tool to support the rising "energy communities". [ABSTRACT FROM AUTHOR]

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Hospitals are among the most energy-intensive buildings in the service industry. The development of energy management strategies could lead to important energy savings, and it must pass through detailed analyses of each specific activity energy requirement. The present study aims to find the main energy drivers of a healthcare facility and to develop an offline monitoring method appliable to future healthcare energy requirements. A Multiple Linear Regression model has been realized to define the standard energy consumption based on the year 2019, allowing to realize a Cumulative Sum of differences control chart including the 2020 energy consumption data. The proposed method allows to find variations between actual and standard building energy demands, being a useful tool to monitor the effectiveness of energy system control strategies.

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system. [ABSTRACT FROM AUTHOR]

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.

A significant buildings energy reduction opportunity exists in the office sector, given that this market segment typically is an early adopter of new technology. There is a rising trend towards smart and connected offices through the internet of things (IoT) that provides new opportunities for operational efficiency and environmental sustainability practices. Leading commercial real estate companies have begun to shift from individual building automation systems (BAS) to partially integrated and automated systems such as energy information systems (EIS). In both the United States and India, organizations are seeking operational excellence, enhanced tenant relationships, and topline growth. Hence it is imperative to engage the executives with decision-making power, by tapping into their interest in sustainability, corporate social responsibility, and innovation. This expansion of interest can enable data-driven decisions, strong energy investments, and deeper energy benefits, and would drive innovation in this field. However, none of this would be possible without robust, consistent building energy information to provide visibility across all the levels of decision making, i.e. from the basement where the facilities staff take operational action to the boardroom where the executives make investment decisions.Price, security, and ease of use remain barriers to the adoption and pervasive use of promising EIS technologies in commercial office buildings. We believe that these barriers can be addressed through the development of ready, simplified, consistent, commercially available, low-cost EIS-in-a-box packages, that have a pre-defined set of hardware components and software features and functionality that are pertinent to a particular building sector. These simplified, sector-specific EIS packages can help to obviate the need for customization, and enhance ease of use, thereby enabling scale-up, in order to facilitate building energy savings. The EIS-in-a-box are adaptable in both U.S. and Indian office buildings, and potentially beyond these two countries.

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user's software platforms. [ABSTRACT FROM AUTHOR]

Energy price and availability have become an urgent global challenge. Manufacturers need to model and reduce energy consumption and cost. To enable energy smart manufacturing using Industry 4.0, there is a need to develop a framework for energy monitoring using Internet of Things (IoT). In this paper, an energy-monitoring device has been developed using ESP32 as IoT system to monitor energy consumption on the fused deposition modelling machine. The energy consumed is measured and categorised according to its manufacturing process states. This paper proposes an IoT system capable of simultaneously measuring the energy consumed in the machine's components during the printing process into idle state, warm-up state, printing state and cool-down state. This platform technology can be a basis for simultaneous monitoring of multiple machines in a production system. [ABSTRACT FROM AUTHOR]