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This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the 'start time' of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin. [ABSTRACT FROM AUTHOR]

Financial development (FD) is pioneered to solve the problem of vital capital supporting for carbon neutrality. Detecting the effect of FD on carbon emissions (CE) plays an important role in accurately planning FD, and guiding carbon neutrality in China. To clarify the influence of FD on CE and the underlying pathways concerning scale effect, structural effect, and technological effect, this paper use China’s 285 city-level data spanning 2003–2016 and fixed effect (FE) models and instrumental variable (IV) methods to test the effect of FD on carbon neutrality in China. The findings show that (1) the development of financial deepening, financial interrelation ratio, and financial intermediary all prompt carbon emissions at the national level. (2) Regional heterogeneity indicates that FD benefits carbon neutrality in mid-Western cities but fails in eastern cities. (3) FD increases carbon emissions by boosting electricity consumption but decreases carbon emissions by promoting structural optimization and technological progress. (4) Environmental regulations partially curb the carbon emissions effect of FD. This paper proposes some suggestions to reduce CE and gain carbon neutrality in China by greening FD, optimizing industrial structure, promoting technologi)cal innovation and strengthening environmental regulation. [ABSTRACT FROM AUTHOR]

At present, electricity demand and pollution are the main concerns all over the world. With the help of this paper, a small contribution to energy production and pollution in the environment can be reduced. Exhaust Fan Cum Micro Wind Turbine (EFCMWT) is used for power generation. In this paper, I study a novel approach of EFCMWT for power generation. It has been installed at a height of 14.11 m at the windows near the toilet in a high rise building at Kosi Hostel NIT Patna. The EFCMWT generates power at a height of 14.11 m in the building. With respect to wind speed, various parameters are analyzed, such as how much DC voltage is generated, how much DC current is generated and also it is analyzed that how much EFCMWT power is generated and tip speed ratio variation with respect to wind speed. On windows where EFCMWT is installed, place anemometer near EFCMWT and analyzed wind power. The power coefficient with respect to wind speed is also analyzed for EFCMWT. Maximum DC voltage is 16 V, maximum DC current is 0.076 A, tip speed ratio is 0.45 and maximum EFCMWT power is 1.216 W which is achieved at wind speed of 7 m/s. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (PSO) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved PSO and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems have generated much interest in the literature, due to the potential synergies with existing hydropower reservoirs. This is of particular relevance to Aotearoa New Zealand, where the national grid's heavy reliance on hydro resources has left the country prone to energy shortages during dry years. This paper investigates the potential implications of a 1-gigawatt peak fleet of distributed FPV systems on seven hydro schemes around the country. The results show that an FPV fleet in Aotearoa New Zealand could provide additional electricity generation when the controlled hydropower storage is low. A larger, wider rollout would allow for the reservoirs to replenish after the country's unique winter peak in electricity demand. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]

Our research aims to find the alteration in electricity consumption during supply chain disturbance following the COVID-19 pandemic, while seeking to elucidate the island-wide power blackout in Taiwan in May 2021. The inquiries addressed are as follows: initially, the income elasticity of electricity consumption decreases over time. We present evidence indicating a decoupling between electricity consumption and GDP, linked to higher overseas production ratio of export orders being produced. Secondly, as a result of the supply chain disruption, the income elasticity of electricity consumption escalates, and Taiwan experiences GDP growth. Electricity consumption increases rise through both channels. Lastly, utilizing a nonlinear model for prediction yields more accurate projections, potentially averting power outages. [ABSTRACT FROM AUTHOR]

A hybrid workstyle is becoming more common post-COVID-19, and longer occupancy hours at home are increasing household electricity consumption. Small homes are regarded as a potential for improving energy efficiency in the residential sector, and a home consists of mixed-function spaces with dynamic occupancy behaviors. These underpin the opportunity to optimize presence sensing lighting in small homes for energy efficiency and user-behavioral needs. A comprehensive overview of presence sensing approaches, comparing four types of non-wearable sensors connected to home lighting is made. A bibliometric mapping of the reviewed literature visually reinforces a significant research gap that presence sensing studies were not connected to home lighting but inclined toward the commercial and institutional context. Next, a non-exhaustive example of commercially available presence sensing products applicable to residential lighting for small homes is analyzed, and their general characteristics and technologies are synthesized. The literature and product overview identified five significant product knowledge gaps. Incorporating the gathered information leads to the proposal of a conceptual flexible radar-based sensor (prototype design), addressing a wish list with three important criteria to optimize future presence sensing lighting in a mixed-function small home. Future radar sensing studies are expected to develop an anticipatory lighting system that processes real-time multi-user vital signals for smarter localized and personalized lighting options for (small) living environments. [ABSTRACT FROM AUTHOR]