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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]

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]

We investigate the power system and climate policy implications of the anticipated expansion in electricity demand from datacentres. We perform a joint optimization of Generation and Transmission Expansion Planning for the island of Ireland considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system. A renewable energy target is more costly than a technology-neutral carbon reduction policy due to the restriction to invest solely in renewable energy, and the divergence in costs increases non-linearly in electricity demand. A carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. The increased demand from datacentres, which is relatively constant, is best matched by a power supply with constant output, generally fossil-fuelled power plants, potentially in combination with carbon capture and sequestration technology, than variable supply. Datacentre demand increases power system costs by 6% under a carbon reduction policy, and by 9% to 15% under renewable energy targets. However, the abatement cost of carbon under renewable targets decreases at higher levels of demand, while the opposite is true for the abatement cost under technology-neutral targets. High renewable targets crowd out other low-carbon options, namely CCS, leading to a higher cost solution for the same reduction in emissions, although this result is sensitive to increases in fossil fuel prices. The results suggest that climate-related energy policy should focus on technology-neutral carbon reduction policies. The climate impacts of datacentres on energy demand and thus supply requirements should also feature in climate and energy policy discussions. Climate policy that focuses on renewable energy only is more expensive than a technology-neutral climate policy The costs of a renewable-only focused climate policy increase non-linearly as demand increases [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]

Global concern over socioeconomic activities' environmental impact is undeniable, given their implications for climate and human welfare. In our study, we analyze Pakistan's landscape, with emphasis on the effects of electricity generation and consumption (both symmetric and asymmetric), energy trade, and the role of information and communication technology (ICT). We harnessed data from 1993 to 2020, sourced from World Development Indicators, Climate Watch, and the USA EIA. Our analysis involved application of the Nonlinear Autoregressive Distributed Lag model and the Autoregressive Distributed Lag bounds testing methods. Our results reveal that electricity consumption, whether experiencing positive or negative shocks, exhibits a tendency to elevate emissions over the long term. Similarly, shocks in electricity generation from fossil fuels, whether positive or negative, have adverse environmental consequences both in the short and long run. A negative shock in electricity generation from renewable and alternative sources triggers environmental degradation. Remarkably, nuclear energy plays a pivotal role in bolstering environmental sustainability in Pakistan. Conversely, we identified factors contributing to ecological degradation, including imports of coal, crude oil, metallurgical coke, and ICT usage, in the long run. Equally, trade and natural resources rent emerge as mitigating factors over time. Ultimately, our study underscores the importance of diversifying the energy mix, with a particular emphasis on expanding the renewable sources for reducing emissions and fostering environmental sustainability in Pakistan. Additionally, our research suggests that leveraging natural resources rent and trade capabilities can pave the way for an eco-friendlier future in the country. This study assesses the environmental impact of electricity generation by source, electricity consumption, energy trade, and ICTs in Pakistan. The findings confirm that both positive and negative shocks in fossil fuel electricity generation and consumption detrimentally affect the environment. Coal, crude oil, metallurgical coke imports, and ICTs degrade the environment, while nuclear electricity generation, natural resources rent, and trade improve it. Integrating energy diversification with focus on promoting renewable energy sources has the potential to significantly reduce emissions and advance long-run environmental sustainability. Strategically utilizing natural resources rent and trade capabilities can aid in establishing an environmental sustainability in Pakistan. [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]

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 study investigates the practicality of integrating renewable energy systems to meet the electricity needs of a self-contained hydroponic greenhouse that functions as an environmentally friendly facility with reliable air conditioning and electrical infrastructure. The first step is to assess the greenhouse’s annual electricity consumption and design a renewable energy system that can sustain this year-round. The results show that daily electricity consumption is between 0.75 and 2 kWh, with thermal air conditioning accounting for over 50% of the total consumption. To address this issue, a simulation model is developed to evaluate the performance of a hybrid photovoltaic/wind system connected to an electric storage battery and to optimize its integration into the greenhouse. This causes the battery’s voltage to generally increase during charging (to 350.5 V) and its state of charge to change non-linearly depending on the battery’s chemical makeup. An Energy Management System (EMS) is then developed for the Hybrid Renewable Energy System (HRES), which works in two modes to ensure the energy balance for uninterrupted power supply. An economic and environmental analysis of three scenarios is developed and concludes that the systems are effective and sustainable, with an estimated payback period of approximately 6 to 8 years. [ABSTRACT FROM AUTHOR]

In this research, proportional-integral (PI) and Fuzzy logic control (FLC) strategies are designed to control wastewater treatment processes at the whole plant level. Two control combination strategies are implemented to minimize the operational cost index (OCI) and effluent quality index (EQI). The benchmark simulation model (BSM2-P) is used as a working platform. In close resemblance to practical operations, the sensors are considered as nonideal with delay and also having measurement noise. Binary-level control topology is designed in which the lower-level PI controller is used to control the dissolved oxygen (DO) levels in the second aerobic reactor by manipulating the oxygen mass transfer coefficient (KLa) of the last three aerobic reactors and FLC is designed at the supervisory level based on Ammonia levels. Ammonia in the second aerobic reactor is taken as a feedback signal for FLC to compute the DO setpoints. In comparison with non-supervisory control structures, operational cost is improved in PI-Fuzzy by 6.5%. Compared to the open-loop control, the PI controller improves effluent quality by 2.1%. Nevertheless, the combination of PI and fuzzy control shows high greenhouse gas production rates. [ABSTRACT FROM AUTHOR]