Energy Monitoring Research Articles

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Implementation Of IoT-Based Sense Plug Design On The Prototype Of The FTTH Network Of The UPI Campus Transmission Laboratory In Purwakarta

The increasing demand for energy presents challenges in efficient energy management. This study aims to develop and implement an Internet of Things (IoT)-based energy monitoring system called SENSE Plug, integrated with a Fiber To The Home (FTTH) network at the Universitas Pendidikan Indonesia campus laboratory. The system is designed to monitor real-time electricity consumption, detect voltage anomalies, and manage the electrical usage of connected electronic devices. The research uses an experimental approach by designing and testing the system in the laboratory. Data was collected using the PZEM-004T sensor connected to the NodeMCU ESP8266 microcontroller, with the measurements displayed on an OLED screen and sent to the IoT Cloud for remote monitoring. The system was tested on various electronic devices to measure voltage, current, active power, and power factor. The results indicate that the SENSE Plug system achieved high accuracy, with an average error of 0.44% for voltage and 0.41% for current. The system can also detect voltage anomalies and automatically disconnect the power supply to prevent damage to devices. In conclusion, SENSE Plug contributes to improving energy efficiency and offers a solution applicable to smart labs and other building environments

Spatiotemporal monitoring of groundwater supply and active energy for irrigation practice in semi-arid regions of Tunisia with machine learning

ABSTRACT Semiarid regions are facing overexploitation of groundwater resources to meet irrigation needs. Monitoring the water-energy nexus allows for optimal management of extracted water volumes and consumed energy. A semiarid zone was selected in the Nabeul region of Tunisia where 14 farmers, whose wells were equipped with smart electricity and water meters (SWEMs), for instant monitoring of pumped water volumes and the electrical energy required for irrigation. Monthly data over a period of 8 months were used to study the variations in water volumes and active energy. The analysis of variance classified farmers into four groups based on water volumes and five groups based on active energy. Spatial variability analysis using kriging showed that the northeast zone is the most solicited in terms of water pumping and energy consumption with water volume exceeding 4,000 m3/month and active energy reaching 2,500 kWh/month. The prediction of energy based on water volume using machine learning techniques such as random forest and support vector machine was successfully conducted. The tools generated by the methodology were applied to a chosen case in the region to estimate active energy and validate the results obtained. The implemented framework allows for better management of groundwater resources for irrigation.

Online application of the periodic linear regression method for daily monitoring of photovoltaic systems performance

This paper presents an analysis of the performance of photovoltaic (PV) systems using daily applications of the periodic linear regression method to the performance indicators Final Yield (YF) and Reference Yield (YR). The research was conducted on PV systems installed on rooftops and ground at the Federal Institute of Education, Science, and Technology of Goiás (IFG), Itumbiara Campus, from July 2022 to August 2023. The Energy Monitoring and Management Software (SMGE) was developed to calculate the performance indicators and perform linear regression to analyze the performance of the systems. The results demonstrate that the efficiency of the systems can be monitored by analyzing variations in the slopes of the linear regression lines. An increase in energy conversion efficiency was observed following the cleaning of the PV panels, as reflected in significant changes in the regression slopes. Additionally, natural events, such as rainfall, positively impacted system efficiency. These findings highlight the importance of proper maintenance of PV panels, whether through scheduled cleaning or natural events like rain.

Personalized Nutrition in the Pediatric ICU: Steering the Shift from Acute Stress to Metabolic Recovery and Rehabilitation.

Nutrition significantly impacts the outcomes of critically ill children in intensive care units (ICUs). Due to the evolving metabolic, neuroendocrine, and immunological disorders associated with severe illness or trauma, there are dynamically changing phases of energy needs requiring tailored macronutrient intake. This study aims to assess the changing dietary needs from the acute phase through recovery, provide recommendations for implementing evidence-based strategies to ensure adequate energy and nutrient provision in pediatric ICUs, and optimize patient outcomes. A comprehensive search of the MEDLINE-PubMed database was conducted, focusing on randomized controlled trials, meta-analyses, and systematic reviews related to the nutrition of critically ill children. The study highlights recent guidelines using the GRADE approach, supplemented by relevant adult studies, current clinical practices, challenges, gaps in knowledge, and future directions for research aimed at improving nutritional interventions. Early personalized, incremental enteral feeding helps mitigate the negative energy balance during the acute phase, aids organ function restoration in the stabilization phase, and supports growth during the recovery phase and beyond. Conversely, early full nutritional support, high protein doses, or isolated micronutrient administration have not demonstrated benefits due to anabolic resistance in these patients. Moreover, early parenteral nutrition during the acute phase may suppress autophagy and lead to worse outcomes. Accurate assessment of nutritional status and monitoring of daily energy and protein needs are crucial. Strong evidence supports the establishment of a dedicated nutritional team and the implementation of individualized nutritional protocols in the ICU to reduce morbidity and mortality in critically ill children.

Firm-Level Digitalization for Sustainability Performance: Evidence from Ningbo City of China

Climate change is a significant and urgent threat, gaining traction in the scientific community around the globe and requiring immediate action across many sectors. In this context, the digital economy could provide a mutually beneficial solution by utilizing innovation and technical breakthroughs to establish a sustainable future that addresses environmental deterioration, promotes economic growth, and encourages energy conservation. Against this background, this study examined the diffusion of innovation modeling-based factors affecting small and medium-sized firms’ (SMFs) adoption of the Internet of Things (IoT) technology and its impact on SMFs’ sustainability performance related to environmental, economic, innovation, and energy conservation perspectives. The key findings revealed that (i) the relative advantage, trialability, and observability drive IoT adoption. However, compatibility and complexity hinder IoT adoption. (ii) When prioritizing the adoption factors, the relative benefit is the strongest driver, and compatibility is the most significant barrier to IoT adoption. (iii) IoT technology adopter SMFs spent less on natural resources and more on renewable energy and environmental monitoring systems than non-adopter firms, boosting their environmental sustainability. (iv) IoT technology adopter firms had greater revenue, profits, and credit access than non-adopters and lower input costs, improving their economic sustainability. (v) IoT adopter firms spent more on innovative products than non-adopter enterprises, demonstrating innovation performance. (vi) Compared to non-adopter firms, IoT technology adopter SMFs had lower utility expenses and spent more on energy-efficient technologies. (vii) To realize the full potential of the IoT for a more sustainable and inventive future, authorities may pursue a variety of policy actions involving the strengthening and implementation of IoT technology standards and regulations, securing the incentivization of financial resources to SMFs, diverting the allocation of resources to research and development avenues, prioritizing the capacity development and environmental awareness, and focusing on IoT infrastructure development.

Practical application of machine learning in energy and thermal management: Long-term data analysis of solar-assisted AC systems in portable cabins in Kuwait and Australia

Machine learning techniques are advancing rapidly in theoretical grounds, but their practical application and real evaluation in engineering disciplines are limited. This study investigates one-year of experimental data for indoor air conditions and energy monitoring of two identical portable cabins in Kuwait. Additionally, a 9-month period of solar photovoltaic energy production is experimentally obtained for assessing energy saving aspects of a solar assisted air conditioning system in one of the cabins. A transient system simulation tool model is developed and validated for the portable cabins. Both one-year experimental data and the validated simulation data are used for developing machine learning models for six case studies in Kuwait and Australia. In simulations, there are issues related to lack of access to real-time weather data from a locally installed weather station. In experimental works, there were issues related to disruption of data collection due to power or internet shutdowns. To resolve these issues, nineteen regression models are investigated. It was found that all models performed very well although the Matern 5/2 and Exponential gaussian process regression model performed slightly better, with all models achieving high accuracy, as indicated by R-squared values close to 1.0 in all six case studies. Overall, it was found that the photovoltaic solar-assisted air conditioning system could save annually 26.2 % and 75.7 % energy in Kuwait and Rockhampton, Australia, respectively.

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

A novel approach to energy management in the dairy industry using performance indicators and load profiles: Application to a cheese dairy plant in Tuscany, Italy

The available energy monitoring information in the dairy industry reveals significant gaps regarding the impact of operational factors on performance indicators and the generation of typical days from load profiles. This paper presents a method for predicting performance indicators and load profiles in the dairy industry based on multiple regression and clustering. The method is applied to a cheese dairy plant located in Tuscany, Italy, providing actionable insights for energy efficiency and renewable integration. With regard to performance indicators, predictions using multiple regression achieved accuracies within 8 % for electricity consumption and within 20 % for steam generation, mainly due to limited data availability. The combination of k-means clustering with multiple regression yielded an overall accuracy within approximately 10 % for electricity load profiles, enabling the labelling of clusters based on production and meteorological variables. The analysis of improved production planning and the compatibility of energy demand with solar resources showed potential reductions in the electrical performance indicator by 27 % and self-consumption rates between 14 % and 42 %, respectively. Validation with data from other dairy contexts confirms the accuracy of the method and the considerable potential for improvement, suggesting further implementation towards effective energy management in the dairy industry.

ANALYSIS OF ENERGY COSUMPTION IN THE NETWORK USING IOT SOLUTIONS

The article explores the importance of implementing IoT systems to analyze energy conditions in current locations. The designed system architecture ensures scalability and flexibility, which allows to effectively collect, save and analyze data. The research results confirm the hypothesis about the value of reduced control and optimization of energy consumption due to automated monitoring and compensation of reactive energy. A practical solution is being developed to automate the monitoring and balance of phase pressures, which promotes energy supply and reduces energy costs.

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology

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.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

IOT based intelligent energy monitoring of grid connected hybrid systems and controlling of loads using PLC

IOT based intelligent energy monitoring of grid connected hybrid systems and controlling of loads using PLC

Feasibility Study and Results from a Baseline Multi-Tool Active Seismic Acquisition for CO2 Monitoring at the Hellisheiði Geothermal Field

CO2 capture and underground storage, combined with geothermal resource exploitation, are vital for future sustainable and renewable energy. The SUCCEED project explores the feasibility of re-injecting CO2 into geothermal fields to enhance production and store CO2 for climate change mitigation. This integration requires novel time-lapse monitoring approaches. At the Hellisheiði geothermal power plant in Iceland, seismic surveys utilizing conventional geophones and a permanent fiber-optic helically wound cable (HWC) for Distributed Acoustic Sensing (DAS) were designed to provide subsurface information and CO2 monitoring. This work details the feasibility study and active seismic acquisition of the baseline survey, focusing on optical fiber sensitivity, seismic modeling, acquisition parameters, source configurations, and quality control. Post-acquisition signal analysis using a novel electromagnetic vibrating source is discussed. The integrated analysis of datasets from co-located sensors improved quality-control performance and geophysical interpretation. The study demonstrates the advantages of using densely sampled DAS data in space by multichannel processing. This experimental work highlights the feasibility of using HWC DAS cables in active surface seismic surveys with an environmentally friendly electromagnetic source, providing also a unique case of joint signal analysis from different types of sensors in high-temperature geothermal areas for energy and CO2 storage monitoring in a time-lapse perspective.

Energy flow tracking of integrated energy system with electric thermal storage boiler

Abstract With the complexity and diversification of the integrated energy system, accurate optimization of resource allocation and scheduling has become the focus of research. This paper studies the energy flow tracking technology of the heat-electric integrated energy system with an electric thermal storage boiler. The modeling of the electric thermal storage boiler and the application of the energy flow tracking technology not only improves the flexibility and stability of the system but also realizes the comprehensive monitoring and analysis of the electric energy and heat energy flow in the system. Finally, the energy flow tracking technology is used to reveal the flow path and distribution of energy in the system through the example of the integrated energy system. It also shows the flexible energy and space-time transfer characteristics of the electric thermal storage boiler in the electrothermal integrated energy system.

Energy conservation strategies on academic buildings using interpretive structural modeling to develop energy sustainability

Effective energy conservation strategies are required to be implemented in academic buildings as they consume significant energy while considering the convenience and function of the buildings. However, the influencing factors are interrelated and complex, requiring an appropriate approach to unravel the complexity. Therefore, this study aims to identify, analyse, and map the interaction relationship between factors affecting energy conservation in academic buildings. This study used Interpretive Structural Modeling (ISM) procedure starting from developing the Structural Self Interaction Matrix (SSIM), converting SSIM into a Reachability Matrix, revising the matrix, and categorizing the factors by using MICMAC analysis. This study involved 9 factors, including architectural design, illumination technology, education and awareness, energy monitoring and management, renewable energy use, efficient HVAC system, energy-saving equipment, institutional policies, and campus community participation. The study found that renewable energy use at level 3 was the factor that was not influenced by and did not interact with any other factors. Meanwhile, the illumination technology was a factor that interacted with the efficient HVAC System factor which was at level 1 where these two factors were influenced by other seven factors. This study aligns with current developments in energy conservation, including an increased focus on renewable energy and energy efficiency in academic buildings, supported by global and national policies aimed at achieving sustainability goals. It provides a comprehensive understanding on developing sustainable energy conservation strategies in academic buildings.

The effect of brief in-survey product experience on preferences for smart energy technologies

Survey research on the adoption of smart energy technologies is growing rapidly, generating important knowledge about factors on the consumer side that may help facilitate transition towards sustainable energy systems. However, much of this research uses survey measures to elicit consumer preferences without explicit consideration of whether and how in-survey experience with the technologies affects preference estimates. For this reason, we experimentally test (for the first time) whether brief in-survey product experience, mainly in the form of additional time spent deliberating about relevant products, influences stated consumer preferences for smart energy monitoring and management apps. Findings obtained in our first experiment conducted in the United Kingdom suggest modest effects of in-survey product experience on consumer preferences, where consumer preferences can be both strengthened or weakened depending on the type of in-survey product experience. These findings are, however, not replicated in our second experiment conducted in Spain. The Spanish experiment, nonetheless, suggests that brief in-survey product experience can help participants make more reasoned choices better reflecting their environmental concern and income constraints. Our results point to possible ways how to improve the reliability of stated preference surveys by providing respondents with adequate in-survey experience with unfamiliar products.

Application of thermal energy efficiency utilization based on computer technology in green manufacturing blockchain production traceability

With the global emphasis on sustainable development, green manufacturing has become a key strategy to enhance economic efficiency and environmental friendliness. In the manufacturing process, the effective use and management of heat energy directly affect the consumption of resources and the protection of the environment. Blockchain technology, with its transparency and immutability, has gradually been introduced into the production process, helping to achieve traceability and optimization of thermal energy utilization. This study aims to explore how to combine computer technology with blockchain to establish a heat energy monitoring and management system based on computer technology, and collect real-time heat energy data through sensors. It then uses blockchain technology to create a transparent production traceability platform, store and manage thermal data, and ensure the security and reliability of the data. The research shows that the system combining computer technology and blockchain significantly improves the utilization efficiency of heat energy, greatly reduces the heat loss in the production process, and significantly improves the reuse rate of resources. The traceability function of the system effectively improves the transparency of the production process and enables enterprises to adjust their production strategies in time. Therefore, the combination of computer-based thermal efficiency utilization with blockchain production traceability offers a new solution for green manufacturing.

Development of real-time energy monitoring system using IoT base

This paper aims to develop a real-time energy monitoring system based on smart metering to enhance energy efficiency in the single-phase residential sector. Based on the concept of low-cost IoT devices, this intelligent meter system is designed to monitor household energy consumption. It provides real-time information on a graphical Node-RED Dashboard, making it easy for households to track and manage their energy usage. The hardware of the power metering system included the Node MCU ESP8266, the PZEM-004T, and a cloud server built on the Raspberry Pi for storing electricity consumption data by using Node-RED to connect devices via an application programming interface system. This system can help analyze the electricity consumption behavior in the residential sector. It is a guideline for selecting the electricity rate between the TOD and TOU rates. The results found that in the electrical energy measurements of households 1 and 2, the mean deviations were 0.8758% and 0.5523%, respectively. The electricity cost-saving results when changing the electricity tariff from the TOD rate of households 1 and 2 to the TOU rate. It was found that electricity costs can be saved by 0.2807% and 1.0936%, respectively. The most critical variable is electricity consumption during peak periods. In the case study of household 1, if the electricity consumption during the peak period is less than 13%, it will be appropriate to select the Time of Use rate. In household 2, if there is electricity consumption during the peak period, less than 38% would be appropriate to choose the type of electricity user with a TOU rate.

Research on Industry 4.0 smart grid monitoring and energy management based on data mining and Internet of Things technology

With the increasing global focus on sustainability and energy efficiency, the concept of Industry 4.0 is becoming an important driver of smart grid technology advancement. Traditional power grids are faced with the challenges of high energy consumption and low management efficiency, and it is urgent to realize intelligent transformation through emerging technologies. This study aims to explore the application of data mining and Internet of Things technology in smart grid monitoring and energy management, with a particular focus on thermal energy management, in order to improve energy utilization efficiency and achieve dynamic regulation. A smart grid monitoring platform integrating data mining and Internet of Things technology is constructed. The data of power and heat energy are collected by sensors, and the data mining algorithm is used to analyze the energy consumption behavior of users and the running state of equipment. A prediction model is also introduced to realize dynamic prediction and optimal scheduling of heat demand. The experimental results show that the thermal energy management system based on the platform can significantly improve the energy utilization rate, data mining technology effectively identifies the peak energy use and potential energy saving opportunities, and the Internet of Things technology realizes real-time monitoring and feedback, enhancing the flexibility and response ability of the system. Through accurate energy monitoring and effective management strategies, it can not only realize the optimal allocation of resources, but also promote the development of enterprises in the direction of low-carbon environmental protection.