Cleaner Energy Systems最新文献

Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.

Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations.

This paper proposes a new hybrid deep learning model to enhance the accuracy of forecasting very short-term wind power generation. The proposed model comprises a convolutional layer, a long-short-term memory (LSTM) unit, and fully connected neural network. Convolution layer can automatically learn complicated features from the raw input, whereas the LSTM layers can retain useful information through which gradient information may flow over extended periods. To obtain the best performance from the forecasting model, a random search optimization technique has been developed for tuning hyper-parameters of the model developed. The 5 min datasets from the White Rock wind farm, Australia are used to investigate the effectiveness of the proposed model as wind farms are participating in spot electricity market. To compare the effectiveness, the proposed model is compared with the existing models, such as convolution neural network (CNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), artificial neural network (ANN), and support vector machine (SVM). The root-mean-square error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (TIC) are used to analyze and compare the performances of the predictive models. Based on RMSE and MAE, the proposed model exhibits a higher accuracy of approximately 23.79% and 28.63% compared to other forecasting methods, respectively.

This research employs a comprehensive Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to investigate the advancement of photovoltaic (PV) energy in Northern Nigeria. The study delves into the intricacies of introducing PV systems within the context of economic challenges, including issues such as currency volatility and inflation, which amplify costs and impede capital investments. Environmental factors, such as dust and sandstorms, are identified as obstacles diminishing the efficiency of solar panels. Additionally, security concerns in remote areas elevate operational costs and influence investment decisions. This paper proposes effective mitigation strategies, encompassing widespread public awareness campaigns to augment market engagement, the establishment of mini-grid systems for enhanced energy distribution, customised on-the-job training programs to foster local expertise in PV technology, and the utilisation of micro-grid systems as experimental grounds for regulatory and policy testing. By synthesising these components, the study offers a comprehensive overview of the prerequisites essential for the successful proliferation of PV energy in Northern Nigeria. Emphasis is placed on the potential for solar energy to significantly contribute to the region's sustainable development and achieve energy independence when the identified strength, and opportunities are exploited. The key strength identified are the average Global horizontal irradiance (GHI) of 5.436 kWh/m², Direct Normal Irradiance (DNI) of 1534–1680 kWh/m², Levelised Cost of Electricity (LCoE) of $ 0.1, and an opportunity to fully utilise the over $ 7.88 million grant authorised by the African Development Bank (AfDB) from the Sustainable Energy Fund for Africa.

The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.

This study presents the optimal design and operation of a proposed hybrid renewable energy system (HRES) for the electrification of a residential building in rural areas in Pakistan. The main contributions of this study are twofold. Firstly, it develops a size optimization model based on the particle swarm optimization (PSO) technique to determine the optimal configuration for two hybrid renewable energy systems (HRES), including both grid-tied and off-grid modes, integrating wind and photovoltaic (PV) systems with battery storage. The optimal configuration is determined by minimizing the levelized cost of electricity, using local meteorological and electricity load data, along with technical specifications of the main HRES components. Secondly, dynamic simulations of two HRES configurations are conducted, using MATLAB Simulink, ensuring the optimal energy balance between multiple energy sources and the load at each operation hour. To meet an annual electrical demand of 131.035 MWh, the grid-tied HRES yields 146.081 MWh annually, with solar contributing 68.85 MWh and wind 77.272 MWh. Conversely, the off-grid system generates 133.533 MWh annually, with solar and wind output power at 43.932 MWh and 89.601 MWh, respectively. The grid-tied system achieves an LCOE of approximately 0.29 $/kWh, with optimal wind turbine and PV capacities of 11 kW and 29 kW, respectively. While in off-grid configuration, the off-grid scenario exhibits an LCOE of 0.91 $/kWh, with optimal capacities of 10 kW for wind turbine, 20 kW for PV, and 2437.5 AH for batteries. The findings provide insights relevant to diverse locations, emphasizing the importance of local meteorological and geographical data. Multiple case studies ensure the robustness and applicability of the proposed system under varying conditions.

Sri Lanka as a country has tremendous potential for harnessing energy from renewable sources such as solar, wind, and hydro. However, as of 2018, only 39 % of Sri Lanka's energy generation capacity was harnessed through renewable energy sources. The continuous increase in electrical energy demand and the drastic increase in vehicle population over the past few years have resulted in much of its annual income being spent on purchasing fossil fuels from foreign countries. This has placed the country's future at risk due to the predicted shortage of fossil fuel reserves and in release of an unexpected level of harmful emissions to the environment. In the meantime, Sri Lanka also has an ambitious plan of achieving Net Zero by 2050. The study conducted a systematic review followed by a time series analysis to first identify the present state of the renewable energy progress of the country and through the time series analysis recognize any discrepancies in these efforts. The initial findings revealed the lack of coordination amongst relevant institutions and contrasting government policies such as the increase in investment for non-renewable energy resources as well as backing away from providing initial investment needed to boost the usage of renewable sources for businesses and smaller entities. The study further identified sectors such as transportation and non-renewable power generation activities as the two main barriers deterring the country from having a feasible plan for its efforts for net zero by 2050. From a non-governmental perspective, the study also recognized the knowledge gap and lack of awareness in the wider population of the long-term benefits of switching to renewable sources.

The growing energy demand in contemporary societies, coupled with the environmental detriments of conventional energy sources, necessitates a shift towards sustainable alternatives such as solar energy. However, the efficiency of solar energy systems is contingent upon various factors including surface orientation, tilt angle, geographic location, climatic conditions, solar irradiation, humidity, and temperature. Nevertheless, dust deposition on the active surfaces of solar energy systems remains the primary factor that highly impacts the system's energy yield, profitability, and efficiency. This paper provides a comprehensive review of the impact of environmental dust accumulation on the performance of solar energy systems that comprise photovoltaic, flat plate collectors, concentrating solar collectors, or solar chimneys. The objectives of this paper extend to consider economic consequences and the cleaning cost due to dust accumulation on the active surfaces of solar energy systems. The annual revenue loss due to dust accumulation was estimated at up to 35 % for 20 % of solar radiation reduction due to dust accumulation and the cleaning costs ranged from 0.016 to 0.9 $/m² worldwide, depending on system type, location, and cleaning technique. The present study offers distinctive perspectives on the topic and provide valuable information to policymakers, researchers, end-users, and stakeholders in the solar energy industry.

The industrial sector is a major energy consumer worldwide. Much of this consumption is due to air conditioning systems. In regions with extreme temperature conditions, the electricity consumption of these air conditioning systems increases significantly. This study was carried out with the objective of calculating the total energy consumption of the factory and identifying methods to decrease it. Furthermore, an enhanced energy system is suggested to lower energy consumption. This study was carried out with the objective of calculating the total energy consumption of the factory and identifying methods to decrease it. Furthermore, an enhanced energy system is suggested to lower energy consumption. It is also evident that the cooling load decreases by 21,661 kWh when thermal insulation is applied to the walls. Utilizing double-glazed windows for the skylight roof can lead to a reduction in the cooling load by 822 kWh. Additionally, the use of Light-Emitting Diode (LED) bulb lamps in the factory can further decrease the cooling load by up to 14,717 kWh.

This work proposes a non-islanded cogeneration energy management center (EMC) that can be used to displace grid-level natural gas turbine systems and natural gas combustion systems for heat. The design of the proposed EMC included a weighted multi-objective optimization aimed at minimizing: i) natural gas consumption; ii) capital costs; iii) utility costs; and iv) unmet thermal demand. The decision variables consisted of the existence and capacity of the equipment comprising the EMC, including: i) a natural gas boiler; ii) an internal combustion engine that generates heat and electricity; and iii) a hot water thermal storage system. Four resulting candidates EMC designs were then compared with the status-quo (SQ) in an eco-technoeconomic analysis; The SQ draws electricity from the grid and heating for dwellings come from natural gas boilers. Emissions at grid level change which alternative is favored. The findings showed that, for a system that serves 4–5 dense urban city blocks over a 20-year lifetime, the SQ system had cumulative levelized costs of 9.6 million USD for the final consumer, while the levelized costs of the EMC designs ranged from 12.9 to 15.1 million USD. In terms of emissions, the SQ emitted 959 tonnes of CO_2eq per year, while the EMC system produced around 500 tonnes of CO_2eq per year depending on the year, yielding a CCA varying between 364 and 653 USD/tonneCO_2eq