Measurement: Energy最新文献_第6页

A review of interface engineering characteristics for high performance perovskite solar cells 高性能过氧化物太阳能电池的界面工程特性综述

Measurement: Energy Pub Date : 2024-04-25 DOI: 10.1016/j.meaene.2024.100005

George G. Njema, Joshua K. Kibet, Silas M. Ngari

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引用次数: 0

Time series forecasting of electricity consumption using hybrid model of recurrent neural networks and genetic algorithms 利用递归神经网络和遗传算法的混合模型对用电量进行时间序列预测

Measurement: Energy Pub Date : 2024-03-30 DOI: 10.1016/j.meaene.2024.100004

Ali Hussein , Mohammed Awad

{"title":"Time series forecasting of electricity consumption using hybrid model of recurrent neural networks and genetic algorithms","authors":"Ali Hussein , Mohammed Awad","doi":"10.1016/j.meaene.2024.100004","DOIUrl":"https://doi.org/10.1016/j.meaene.2024.100004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"2 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950345024000046/pdfft?md5=70db8d45f1ec7ae86558a660d420846b&pid=1-s2.0-S2950345024000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An innovative noise reduction blower fan housing design used in electronics cooling 用于电子设备冷却的创新型降噪鼓风机外壳设计

Measurement: Energy Pub Date : 2024-03-01 DOI: 10.1016/j.meaene.2024.100002

Mohammed Amer

{"title":"An innovative noise reduction blower fan housing design used in electronics cooling","authors":"Mohammed Amer","doi":"10.1016/j.meaene.2024.100002","DOIUrl":"10.1016/j.meaene.2024.100002","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"1 1","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950345024000022/pdfft?md5=24c6fcd2489f1163bddaa30ac27e0d5f&pid=1-s2.0-S2950345024000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Price forecasting through neural networks for crude oil, heating oil, and natural gas 通过神经网络预测原油、取暖油和天然气的价格

Measurement: Energy Pub Date : 2024-03-01 DOI: 10.1016/j.meaene.2024.100001

Bingzi Jin , Xiaojie Xu

{"title":"Price forecasting through neural networks for crude oil, heating oil, and natural gas","authors":"Bingzi Jin , Xiaojie Xu","doi":"10.1016/j.meaene.2024.100001","DOIUrl":"10.1016/j.meaene.2024.100001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100897,"journal":{"name":"Measurement: Energy","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950345024000010/pdfft?md5=45337b14a00f3f00be24e6c7e3097445&pid=1-s2.0-S2950345024000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140069530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0