Search Results (74)

Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids. Full article

The search for sustainable energy alternatives is urgent in the face of climate change and resource scarcity. In this context, increasing energy efficiency in buildings through distributed energy resources (DERs) is crucial for sustainability and self-sufficiency. This article aims to analyze the impact of several energy efficiency actions, in addition to the installation of a photovoltaic solar energy system in a public building in a semi-arid region, determining the necessary aspects for such buildings to become positive energy buildings (PEBs) and/or net zero energy buildings (NZEBs). As a basis for the methodology, a case study was carried out in a university restaurant in a semi-arid region in Brazil. Several pieces of data were collected, such as the air temperature, solar radiation, active energy and the number of users in the building. The relevance of each variable in relation to electricity consumption was identified through statistical correlation analysis, resulting in an energy consumption per square meter per year of 80.78 kWh/m²/year and an active energy consumption per user per year of 0.88 kWh/m²/year. Three energy efficiency actions were evaluated and compared technically and economically against the investment in a grid-connected photovoltaic system (GCPVS) for the same building, simulating before and after the entry into force of Law 14.300/2022, which regulates distributed generation in Brazil. The installation of thermal insulation on the building’s roof showed good technical, economic and environmental performance, compared to GCPVS, and proved to be attractive and competitive among the other scenarios. All simulated actions resulted in an annual emission reduction of 14.8 tCO₂e. When calculating the building’s generation potential, it was found that it could be considered an NZEB and PEB. Full article

This research evaluates the economic feasibility of implementing smart metering (SM) systems in Peruvian electricity distribution companies, prioritizing the maximization of the benefit–cost ratio (BCR). Seven communication architectures were analyzed in four companies, considering variables such as energy losses, meter costs, and per capita consumption. The results, evaluated through economic indicators such as the net present value, internal rate of return (IRR), and BCR showed that Luz Del Sur (LDS) obtained the best results, while ADINELSA (an electrical infrastructure management company), Sociedad Eléctrica Sur Oeste (SEAL), and Electro Sur Este (ELSE) presented the worst. The combination of power line communication and general packet radio service was the most viable architecture, followed by radio frequency mesh. However, this study concludes that a massive deployment of SM in Peru is not yet economically viable because of low per capita consumption and high meter costs. Future research should consider the benefits of distributed generation and demand management, as well as evaluate new communication technologies. Full article

The objective of this study was to evaluate the influence of the mean individual volume per tree (MIV) on the productivity of forwarder machines and the production cost in eucalyptus plantations located in southern Bahia, Brazil. MIV positively influenced the productivity and production costs, promoting a more attractive cost in the latter when the individual volume per tree increased. The machine’s productivity for MIV of 0.13 m³ was 42.06 cubic meters per effective working hour (m³Ewh⁻¹), while the productivity for the MIV of 0.58 m³ reached 60.97 m³Ewh⁻¹, corresponding to an increase of 42.59% between the minimum and maximum MIV classes. The extracted cost (m³) decreased by 30.12% from USD 2.49 to 1.74, respectively, when comparing the minimum and maximum MIV classes. The coefficient of determination obtained in the forwarder productivity modeling was significant (R² = 92%), indicating the machine’s productivity can be explained by the mean individual volume per tree. The highest forwarder yields in the highest average volume per tree (MIV) classes provided better energy efficiency indices for the machine; that is to say, when the forwarder became more productive, the ratio between fuel consumption per cubic meter of timber harvested decreased, providing better performance for the respective index. There was a difference in extraction costs of USD 147.83 per hectare between the lowest and highest productivity forests (MIV varying from 0.15 to 0.58). The mechanical availability and mean operational efficiency of all forwarders evaluated were above 80%, which contributed to effective machine productivity performance. Maintenance and repairs represented the largest portion of operational costs (33.59%), followed by labor (22.49%), depreciation (14.33%), and fuel (10.11%). Full article

Adopting photovoltaic (PV) systems in government schools across Saudi Arabia presents an opportunity to reduce energy costs and contribute to the country’s RE goals. In this paper, the energy consumption and energy consumption indicators of 3 schools in Qassim region (the central region of the Kingdom of Saudi Arabia) were determined. The integration of PV systems into the buildings of these schools was also studied to achieve zero energy and zero bills along the system’s life cycle. The analysis considered the effects of temperature and other factors on PV system output and a projected 1% annual increase in school load. Energy use intensity ranged from 22 to 48 kWh per square meter, while per capita energy use varied between 337 and 630 kWh. Values for end-use of electricity of 80%, 11%, and 9% were obtained for air conditioning, lighting, and others, respectively. The results note that the zero-energy scenario’s technical, economic, and environmental indicators are appropriate. The indicators in the zero-billing case were similar to the energy-zero scenario except for the payback period, which was longer and, in some cases, not economically feasible. The results show that economic evaluation must be revisited by reviewing the tariff value for selling surplus energy to the distribution network. The study also recommends scaling this model to other educational institutions, contributing to sustainable energy transitions in Saudi Arabia. Full article

Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE. Full article

This study presents a comprehensive analysis of the energy efficiency and sustainability of Variable Refrigerant Flow (VRF) systems in university buildings during the winter season, offering significant contributions to the field. A novel methodology is introduced to accurately assess the real Seasonal Coefficient of Performance (SCOP) of VRF systems, benchmarked against conventional Heating, Ventilation, and Air Conditioning (HVAC) technologies, such as natural gas-fueled boiler systems. The findings demonstrate outstanding seasonal energy performance, with the VRF system achieving a SCOP of 5.349, resulting in substantial energy savings and enhanced sustainability. Key outcomes include a 67% reduction in primary energy consumption and a 79% decrease in greenhouse gas emissions per square meter when compared to traditional boiler systems. Furthermore, VRF systems meet 83% of the building’s energy demand through renewable energy sources, exceeding the regulatory SCOP threshold of 2.5. These results underscore the transformative potential of VRF systems in achieving nearly Zero-Energy Building (nZEB) objectives, illustrating their ability to exceed stringent sustainability standards. The research emphasizes the strategic importance of adopting advanced HVAC solutions, particularly in regions with high heating demands, such as those characterized by continental climates. VRF systems emerge as a superior alternative, optimizing energy consumption while significantly reducing the environmental footprint of buildings. By contributing to global sustainable development and climate change mitigation efforts, this study advocates for the widespread adoption of VRF systems, positioning them as a critical component in the transition toward a sustainable, zero-energy building future. Full article

Steel is an important construction material in civil engineering. In addition, the building industry is one of the global economy’s largest sectors, responsible for one-third of the energy consumption and significant CO₂ emissions. For this reason, there is a need to design effective structures that are characterized by the lowest possible steel consumption. This article presents an approach to sustainability considerations in steel structures, namely the approach of shaping efficient steel canopies with modular roofs using genetic algorithms. The shed structures, which were designed based on a regular polygonal plan, were constructed from grid modules that were formed on the basis of the hyperbolic paraboloid (HP) units arranged radially, supported by the columns, and covered by metal sheets. The algorithmic definitions allowed for the creation of numerous variants of the structures with the adopted preliminary criteria and for the performance of genetic optimization in order to select the best results. Twenty-four kinds of structures were analyzed and compared, differing in the quantity of modules, module shapes, arrangements, and dimensions. This made it possible to observe changes in the efficiency of the structures depending on the form of the roof applied. As a measure of structural efficiency, the coefficient representing the mass of the shed structure per square meter of the covered area was utilized. The presented design approach and optimal solutions can be helpful in shaping more complex sustainable structures, for which the analyzed sheds constitute modules. Full article

Electricity consumption in buildings is a significant contributor to greenhouse gas emissions, which drive climate change. Reducing electricity use in residential buildings, which account for approximately 20% of Thailand’s total electricity consumption, represents a key opportunity for lowering greenhouse gas emissions. The aim of this study was to assess the potential reduction in greenhouse gas emissions through the use of appropriate solar absorptance in wall paint, conducted via an energy simulation using a representative residential building model from Bangkok. The DOE2.1E program was employed to simulate a standard two-story house commonly found in Thailand, with an approximate floor area of 120 square meters. The window-to-wall ratios were set at 10% and 20%, and air conditioning usage was modeled for nighttime hours. External wall paint was assigned varying solar absorption coefficients, ranging from 10% to 90%. Greenhouse gas emissions were calculated by multiplying the simulated annual electricity consumption by the emission factor, expressed in kgCO₂eq/kWh, provided by the Thailand Greenhouse Gas Management Organization. The results indicated that adjusting wall paint solar absorptance from 10% to 90% led to a 10% variation in both energy consumption and greenhouse gas emissions, potentially reducing CO₂ emissions by approximately 411–456 kgCO₂eq per house per year. Therefore, implementing regulations that mandate the use of wall paints with appropriate solar absorption coefficients could significantly reduce greenhouse gas emissions and contribute to environmental protection efforts in Thailand. Full article

Increasing the use of renewable biofuels in internal-combustion-engine (ICE) vehicles is a key strategy for reducing greenhouse gas emissions and conserving fossil fuels. Hybrid vehicles used in urban environments significantly reduce fuel consumption compared to conventional internal-combustion-engine cars. In hybrid vehicles integrating electric propulsion with biofuels offers even more significant potential to lower fuel consumption. One would like to think they should also be less polluted in all cases, but some results show that the opposite is true. This study’s aim was to evaluate a hybrid vehicle’s energy and environmental performance using different gasoline–bioethanol blends. A Worldwide Harmonized Light Vehicles Test Cycle (WLTC) study was conducted on a Toyota Prius II hybrid vehicle to assess changes in energy and environmental performance. During the WLTC test, data were collected from the chassis dynamometer, exhaust gas analyser, fuel consumption meter, and engine control unit (ECU). The collected data were synchronised, and calculations were performed to determine the ICE cycle work, brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), pollutant emissions (CO, HC, and NO_x), CO₂ mass emissions per cycle, and brake specific pollutant emissions per kilometre. The study shows that the performance of the hybrid vehicle’s ICE is strongly influenced by the utilisation of electrical energy stored in the battery, especially at low and medium speeds. As the bioethanol concentration increases, the engine’s ECU advances the ignition timing based on the knock sensor signal. A comprehensive evaluation using the WLTC indicates that increasing the bioethanol concentration up to 70% improves the energy efficiency of the hybrid vehicle’s internal combustion engine and reduces pollutant and CO₂ emissions. Full article

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)GPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 $\mathsf{\mu }$Wh per day on average). Full article

The wide variation in household characteristics, such as household size, income, and age, can lead to significant differences in carbon footprints. Based on data from 1132 Chinese households in 2021, this study examines the structural differences, multiple influencing factors, and mitigation strategies of household carbon footprints (HCFs) in China. The results indicate that indirect emissions, primarily from energy and food consumption, account for the largest share of household carbon footprints, making up over 65% of total emissions. Households with lower carbon footprints are characterized by a per capita living area of less than 20 square meters, rural residences, and shared living arrangements. Carbon footprints for the elderly and minors are lower than adults, while households with higher monthly incomes have the highest carbon footprints. The Multivariate Analysis of Variance (MANOVA) reveals that the main factors influencing HCF include household size, income, and single status, with a more pronounced impact on affluent households than on average households. High-income households have the potential to reduce their carbon footprints through investments in energy-efficient technologies, whereas low-income households are more susceptible to the effects of household size and geographic location. It is recommended that policymakers adopt differentiated measures, such as setting higher reduction targets for larger and wealthier households while providing incentives and technical support to low-income households to achieve meaningful carbon reductions. More effective and equitable low-carbon policies can be formulated by addressing these structural disparities and leveraging the unique characteristics of different household types. Full article

This research presents a solution for improving energy efficiency in convenience stores by implementing a building energy management system (BEMS) that uses new logic control in air conditioning and refrigeration systems. These systems currently consume the most energy in convenience stores. Implementing this system not only reduces the energy consumption of the compressors in both systems but also minimizes energy loss due to low desired temperatures in the sale area while maintaining the cabinet temperature at the same level. An experiment was conducted at a 314-square-meter convenience store that was open from 6:00 a.m. to 11:00 p.m., and we demonstrated a 4.4-year payback period by controlling AC units close to the desired sale-area temperature of 25 degrees Celsius and increasing the suction pressure at a medium-temperature CDU by 0.3 bar or 31 kPa. This resulted in energy savings of 7.1 kilowatt-hours per day, or 2591.5 kilowatt-hours per year, for the air conditioning system and 2.8 kilowatt-hours per day, or 1022.0 kilowatt-hours per year, for the refrigeration system, resulting in a total energy savings of 9.9 kilowatt-hours per day, or 3613.5 kilowatt-hours per year. The convenience store can use the results of this research to improve the energy efficiency of its cooling system, which includes air conditioning and refrigeration systems, thereby promoting sustainable energy conservation. Full article

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. Full article

With the further establishment of relevant regulations on ship emissions by countries worldwide and the IMO, and the increasing frequency of severe sea conditions in shipping routes, optimizing ship energy efficiency under high wind and wave conditions has become an important research direction. This study establishes a grey-box model for optimizing ships’ energy consumption under severe sea conditions, with wave heights above two meters and a Beaufort scale score above five, based on the principle of ship–engine–propeller matching and a non-dominated sorting optimization algorithm. Using historical navigation data from a case ship under severe sea conditions, a white-box model and a black-box model for ship fuel consumption were established. These models were combined to create a grey-box model for ship fuel consumption. The K-Medoids clustering algorithm was used to cluster severe sea conditions. The optimization variables were the main engine’s speed, with the fuel consumption per nautical mile and the ship’s speed being used as optimization objectives. The non-dominated sorting genetic algorithm was optimized for each sea condition, resulting in the best speed for each sea state. The results indicate that the model developed in this paper reduced the main engine’s fuel consumption per nautical mile by 21.9% and increased the speed by 16.7% under the most severe sea conditions. Therefore, the proposed model effectively optimizes ship energy efficiency and reduces navigation time under severe sea conditions, providing an effective solution for operations in actual severe sea conditions. Full article