Measurement: Energy最新文献

In order to maximize the solar radiations falling on a Photo-voltaic (PV) panel and hence, to maximize the solar power generation, an optimum tilt angle of the PV panels for a specific geographic location plays a critical role. This paper exploits the tilt angle and establishes an empirical relation among optimum tilt angle, module temperature and ambient temperature. Moreover, estimating accurate solar photovoltaic power output depends on the correct modelling of the PV module. Temperature and irradiance dependent modelling need statistical support for their behaviour and pattern. This work also examines and institutes the relationship between Ambient temperature and Module temperature throughout the year. Furthermore, in order to determine the impact of irradiance, ambient temperature and module temperature on the solar power generation of a grid-connected solar power plant, this paper evaluates Karl Pearson correlation coefficients for each of the following three pairs (1) generation and irradiance (2) generation and ambient temperature and (3) generation and module temperature, for all the 12 months of a year. The results obtained shall help to better understand, manage, plan, forecast and stabilize the solar power output. Earlier researchers usually used weather data for their study, which are not location-specific therefore, accuracy is questionable. Hence, the data used in this research is recorded from a 3.3 MWp grid-tied ground-installed solar power plant and a 119 ​KW grid-tied rooftop-installed solar power plant, both located at Aligarh Muslim University, Aligarh, India. This paper presents an exhaustive analysis of the two grid-tied solar power plants as there is very little work with actual data of generation, irradiance, temperature and tilt angle, all measured on the spot with high accuracy; results obtained are realistic with a novel approach.

Proof of concept is established for the thermal management of PV modules for the simultaneous benefit of electrical and thermal efficiency. It was achieved by designing the controlled open-loop water-based hybrid PV-T system and demonstrated for thermal management of 150W photovoltaic panel at natural solar conditions. In this integrated front glass-covered PV-T hybrid (IFG-PV-T) system, the PV panel is clipped between the front glass and rear aluminium collector without causing any permanent changes in the existing panel. The flow of water from the source tank is controlled by automated solenoid valve assembly coupled with thermocouples. The solenoid operational temperature is fixed at 40°C to controlled the PV surface temperature at an optimum range to mitigate the adverse effect on voltage and current output. The water layer thickness in the front glass box is optimized to filter the maximum infrared radiations and the collector's toughened glass feature allows the maximum light transmittance with super safety. The performance of the IFG-PV-T system has been evaluated in terms of variation in open circuit voltage, short circuit current, maximum power output, electric and thermal efficiency under the natural solar insolation for a week. Experimental investigations revealed that the open-circuit voltage is increased by ∼16.1 % with IFG-PV-T as compared to conventional PV panel. The increment can be attributed to the synergy of the front glass collector and solenoid valve operation at 40^oC that regulates PV surface temperature and boost the current output. These factors have ameliorated the electrical efficiency of IFG-PV-T compared to conventional PV panel. The average thermal efficiency was 39.4 ​% wherein the IFG-PV-T system provides ∼100 ​L of hot water (38–41 ​°C) per day. The present controlled loop operation system and collector configuration have proven their significance for electrical power increment and concomitantly able to deliver moderate hot water which can be useful for any household or commercial application.

The use of perovskite solar cells (PSCs) holds immense promise in electricity generation due to their high efficiency and potential for cost-effective production. However, their practical application faces limitations due to issues like sensitivity to moisture, ion migration, and interface defects, affecting their stability and lifespan. This work delves into the critical role of interface materials in enhancing the stability and effectiveness of perovskite solar cells. Techniques such as passivation and encapsulation designed to mitigate these challenges are comprehensively explored. The study investigates the root causes of perovskite deterioration and how engineering interfaces can bolster the durability of these devices. Various methods for passivation, including surface modification, self-assembled monolayers, and utilizing materials with wide band gaps, are scrutinized for their ability to reduce defects and control degradation problems. Furthermore, strategies involving barrier films, polymers, and hybrid inorganic-organic materials are evaluated for their potential to shield perovskite layers from moisture and environmental influences, thereby prolonging the devices' lifetime. The interconnected nature of passivation layers, encapsulation techniques, and their suitability for large-scale manufacturing processes are presented. The analysis outlines the challenges and opportunities in developing interface materials for perovskite solar cells, considering the trade-offs between device performance, stability, and affordability. Accordingly, potential future pathways and emerging trends in interface engineering for the next generation of perovskite solar cells are suggested, aimed at propelling these devices towards commercial success by achieving high efficiency and long-term stability.

The forceful energy efficiency to manage the demand is essential to meet development goals. Palestine has suffered from an electricity deficit, whereas the city of Tulkarm suffers from a chronic one. The dataset was collected from Tulkarm city in Palestine; this city is considered one of the cities that suffers the most from frequent power outages. It's difficult to determine the most powerful Artificial intelligence (AI) approaches that can accurately forecast electricity consumption. This paper presents a hybrid model that combines Recurrence Neural Networks (RNNs) and Genetic Algorithms (GAs) [RNN-GAs] to forecast electricity consumption and optimize demand. In the proposed model the K-means clustering technique produces specific initial population seeding and optimization crossover operators to enhance the efficiency and find the optimal solution. The results showed that the proposed Nonlinear Autoregressive with External (Exogenous) (NARX) (NARX-GAs) with the K-means clustering technique outperforms the hybrid model NARX-GAs. The NARX-GAs-K Mean Clustering recorded an RMSE value of 0.08759, which performs a good balance with the lowest RMSE, especially in long-term forecasting, and also outperforms the other hybrid forecasting models that depend on RNN-GAs. Finally, the forecasting results of the hybrid NARX-GAs-K Mean Clustering can predict accurately the energy consumption in a city, which leads to the use of the model in similar cities to forecast and manage the demand for electricity consumption.

Electronic devices are equipped with blower fans as a means of removing the heat that accumulates in them. This type of fan operates smartly by increasing the speed of the impeller as the electronic devices become overloaded. When the speed of the motor increases, it creates unwanted noise that may be harmful to the ears of the user. Therefore, it is imperative to reduce this noise while maintaining the same dimensions of the fans. The purpose of this work is to demonstrate how critical measurements can be used to improve the design of blower fan housings. By making a change in the housing of the fan, this study proposes an innovative solution to the noise problem associated with heat radiation fans. A punch has been added to the new housing of notebook system, which may be located on either the upper or lower sides. A punch should be located at the air inlet on the fan's air outlet side, between 0 and 90°. Moreover, a punch should have a height ranging from 0.3 to 1 ​mm and a circle size ranging from one eighth to one fourth. Additionally, the details of noise measurement are presented. The results of the study showed that the noise reduction was enhanced by more than 2 ​dB(A) which can either result in a performance enhancement by increasing the flow rate to reach the same flow rate as the original fan or in a decrease in human discomfort by lowering the noise level. The work has been patented under patent numbers TWM624190U, and CN216554487U.

Building price projections of various energy commodities has long been an important endeavor for a wide range of participants in the energy market. We study the forecast problem in this paper by concentrating on four significant energy commodities. Using nonlinear autoregressive neural network models, we investigate the daily prices of WTI and Brent crude oil as well as the monthly prices of Henry Hub natural gas and New York Harbor No. 2 heating oil. We investigate prediction performance resulting from various model configurations, including training techniques, hidden neurons, delays, and data segmentation. Based on the investigation, relatively straightforward models are built that yield quite accurate and reliable performance. Specifically, performance in terms of relative root mean square errors is 1.96%/1.81%/9.75%/21.76%, 1.96%/1.80%/8.76%/14.41%, and 1.87%/1.78%/9.10%/16.97% for model training, validation, and testing, respectively, and the overall relative root mean square error is 1.95%/1.80%/9.51%/20.35% for the whole sample for WTI crude oil/Brent crude oil/New York Harbor No. 2 heating oil/Henry Hub natural gas. The outcomes of this projection might be used in technical analysis or integrated with other fundamental forecasts for policy analysis.