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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model's superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain-based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph-based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real-world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

Buildings play a critical role in energy consumption, representing one of the primary consumers of power. Heating load (HL) and cooling load (CL) are essential for determining the energy efficiency of buildings. Several research projects attempt to address the critical challenge of enhancing energy efficiency in residential buildings, focusing on accurately estimating HL and CL using solutions that implement statistical prediction or typical building control management. This study, however, looked into advanced machine learning (ML) models for sustainable building design based on harnessing the potential of artificial intelligence and explainable AI (AIX) technologies. The proposed model was trained and tested using a dataset of 768 buildings based on feature engineering methods with various ML algorithms (including cutting-edge emotional neural learning (ENN), nonparametric kernel-based probabilistic models known as Gaussian process regression (GPR), and boosted tree (BT) algorithm). In addition, the output of the model was fed to standard building energy performance software (Ecotect) that utilizes the dataset from twelve different building shapes to perform various building energy efficiency analyses. The overall performance of the proposed model was measured using different performance metrics, including MAPE, MAE, RMSE, and PCC to measure the performance of HL- and CL-based building energy efficiency. The performance evaluation results indicate that the M3 variants, especially GPR-M3, consistently outperformed their counterparts across heating and cooling scenarios. The three models indicated reliability for modeling HL and CL. However, for HL, the GPR-M3 model emerged as the best model, outperforming GPR-M1 by 9.2% and GPR-M2 by 3.9%. Similarly, GPR-M3 is superior to CL, with the highest PCC at 0.9858, marking an 8.1% and 1.9% improvement over GPR-M1 and GPR-M2, respectively. [ABSTRACT FROM AUTHOR]

This research showcases the seamless integration of solid oxide fuel cells (SOFC) with waste heat recovery systems, utilizing liquefied petroleum gas (LPG) as the primary fuel source. The focus of the study is on efficiently harnessing the cold energy from the LPG supply to produce substantial power for the entire system. To optimize energy utilization, a gas turbine (GT) and steam Rankine cycle (SRC) are integrated to effectively convert waste heat from the system into useful work and power. Additionally, a waste heat boiler (WHB) is incorporated to provide superheated vapor steam for seafarer accommodation. A detailed thermodynamic analysis and investigation of the proposed integrated system are performed. The simulations and optimizations of combined system are conducted using ASPEN HYSYS V12.1. Thermodynamic equations based on the fundamental laws of thermodynamics are employed to estimate system performance indicators, and the exergy destruction in major components is assessed to optimize the system's design and operation. The proposed system exhibits impressive energy and exergy efficiencies, calculated at 52.65% and 51.10%, respectively. Moreover, the waste heat recovery combined cycles contribute an additional 1,759.73 kW, equivalent to 31.65% of the total system output. The innovative models are validated against experimental data from the literature, demonstrating strong agreement. Furthermore, a comprehensive parametric study investigates the influence of varying the current density from 900 to 1,950 A/m2, leading to a total energy efficiency variation of 41.59%, ranging from 82.45% to 40.86%. The organic Rankine cycle (ORC) performs exceptionally well, capitalizing on both cold energy and high-temperature waste heat to achieve high energy recovery efficiency. The WHB is capable of providing 8,200 kg/h of superheated vapor stream at 151°C and 499 kPa for seafarer accommodation and heating purposes. The economic viability analysis is conducted to assess the potential for investment, maintenance costs, and the payback period associated with the proposed system. [ABSTRACT FROM AUTHOR]

The choice of mechanical and electrical topologies can affect piezoelectric energy harvesting efficiency, but the problem of achieving high-efficiency energy conversion in energy harvesters stacked as cantilevers has not been perfectly solved. This study focuses on the topology of piezoelectric elements in a stacked vehicle suspension vibration energy harvesting device. Through theoretical analysis, the stress expressions of the excited and driven elements are derived. The stress of the piezoelectric elements is affected by the positions of the connection and excitation points. A stress model was established for a four-piece piezoelectric element connected by a thin light rod in ANASYS. The simulation results show that the average stress in the piezoelectric bending element model is maximum when the excitation and connection points are located at both ends of the free end. Compared with the middle position of the free end of the piezoelectric element, the average stress of the model is increased by 92.904%. Considering the difference in voltage generated by piezoelectric elements, four kinds of electrical topology are designed and analyzed experimentally. When the driven elements are connected in parallel and then connected in series with the excited element, the output power varied the least with the change of load resistance. The system produces a high power and offers a wide selection of load resistors. When the load is 26 kΩ, a single set of four piezoelectric elements produces 86.407 mW of output power. [ABSTRACT FROM AUTHOR]

Facing the method's limitations of the existing drone inspection on offshore wind farms, we adopt a new comprehensive-assisted drone automated inspection scheme under the comprehensive assistance. Our objectives are saving energy and high-efficient inspection. The such inspection is used to formulating the two mixed-integer nonlinear programming problems based on two new drone basic models: the mobile edge computing driven drone computation system model and the drone flight model. To solve the problems, we split them into four subproblems, and a new improved heuristic algorithm is created to address. In turns, the waypoints, total inspection time, inspection energy consumption, and traveling distance of unmanned aircraft vehicle (UAV) and the traveling distance of boat are obtained by K -means algorithm and the smallest enclosing circle (SEC) algorithm, the Lin-Kernighan Heuristic 3 (LKH-3) algorithm, and the LKH. Finally, conducting the comprehensive optimization and simulation, the simulation numeric results are gotten. The simulation results demonstrate that as for the two aspects of the total energy consumption and inspection efficiency under different data amount and average wind speed, the scheme improves at most 44% and 23.5% than the current other three; the scheme achieves the objective of saving energy and high-efficient inspection. [ABSTRACT FROM AUTHOR]