浅谈供电企业抄核收存在的问题及处理措施

为了提高企业的综合竞争力，以佛山局为例，从供电企业基础工作抓起，做好抄核收工作，在提高经济效益的同时，提升企业的竞争能力。阐述了供电企业抄核收工作对提升企业综合竞争力的重要意义，分析了供电企业现阶段抄核收部门工作中存在的问题，并提出了相应的处理措施，仅供参考。     

高固气比技术在2 500 t/d水泥熟料线的改造案例介绍*

采用高固气比悬浮预热分解技术对宝江2 500 t/d生产线烧成系统进行技改后,预热系统出口温度从351℃降低至279℃,粉尘浓度(标况下,以下同)从151.6 g/m3减少至43.9 g/m3,系统分离效率大幅提高;入窑生料分解率从90.5%提高至98%,生产线熟料烧成煤耗从119.4kg/t降低至100.80kg/t,熟料产量从2744t/d提高至3840t/d,各指标均属领先水平。     

Electricity Production

Current data on net electricity production, as well as a breakdown of production by country. Updated on a monthly basis.     

典型实验房间空调气流组织监测方案设计研究

有效地通风和合理的气流组织对于改善室内空气品质,控制室内污染物浓度,对实现健康建筑、舒适性空调有着重要的意义。室内空调气流组织方式多种多样,对其效果的研究也很多,但是多集中在理论分析,而对气流组织效果的监测方面的研究较少,且监测方法没有一个完成的体系。首先对空调气流组织现场监测和评价方法进行分析,得知现场检测方法多以工程标准,只是对室内气流组织的应用效果进行评价,并不能完全反映气流组织的轨迹特征。其次,为了更好的反映不同时刻空调气流组织的特征,建立气流组织的监测方法,以5m×3m×2.8m的一个典型实验房间为例,利用计算模拟方法,分析不同气流组织形式下温度、风速等衰减特征,为室内气流组织现场智能监测测点布置提供依据。     

灭火器的定位和有效性辨识研究

智慧消防是智慧城市建设的重要组成部分，在消防工作创新发展中发挥重要作用。灭火器作为扑救初期火灾的最普遍、最便捷和最广泛使用的灭火设施，在初期火灾的扑救和火灾防控中发挥重要的作用，其有效性和完好性是扑救初期火灾的必要条件。基于物联网及大数据的优势，建立消防灭火设施的定位与有效性辨识系统，并介绍系统的关键技术、网络架构、技术特征。此系统可对灭火器等消防灭火设施的伺应状态、完好性、有效性等功能特性进行跟踪巡检和辨识，以实现对灭火器的实时监控和智能化管理，促进消防设施建设的完善和优化，增强灭火系统的可靠性，充分发挥智慧消防技术在智慧城市建设中的优势。     

智能卡车调度系统在紫金山露天矿的应用

紫金山露天矿采用多种开拓运输方式,设备大型化、机械化程度较高,但采矿生产过程存在机械设备作业范围大、作业面广、作业点多的特征,安全管理、生产管理形势严峻。本文立足紫金山生产模式,针对该矿智能卡车调度系统的建设,提出进一步完善卡调系统运行管理、提高露采数字化管控水平的措施,旨在充分发挥卡调系统对生产的智能辅助,提升露天采矿安全管理、生产管理、资源管理及成本管理水平,打造新时期智慧矿山建设新标杆。     

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques

Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage applications. The hydrogen storage capacity of this structure is typically related to pressure, temperature, surface area, and adsorption enthalpy. Literature provides no reliable correlation for estimating the hydrogen uptake capacity of MOFs from these easy-measured variables. Therefore, this study introduces several straightforward and accurate artificial intelligence (AI) techniques to fill this gap, initially determining the appropriate topology of AI-based methods, then comparing their performances by statistical criteria, and introducing the most accurate. This study used artificial neural networks, hybrid neuro-fuzzy systems, and support vector machines as estimators. The general regression neural networks (GRNN) with a spread of 7.92 × 10⁻⁴ shows the highest correlation with the literature data and provides a relative absolute deviation of 5.34%, mean squared error of 0.059, and coefficient of determination of 0.9946.     

When less is more – Effects of providing simple vs. refined action-knowledge interventions to promote climate-friendly food consumption in German consumers

A crucial challenge in global climate protection involves figuring out how to promote climate-friendly (CF) food consumption in high-income countries. Improving consumers’ ability to identify CF food is a promising way to reach this goal. Previous research has shown that providing action-knowledge is an intervention technique that can effectively improve the mentioned ability if relevant preconditions are considered. The present study (further) examined such preconditions and possible refinements of this intervention technique in a representative sample of German consumers (N = 510). Participants were randomly assigned to one of two experimental groups (EG1 received simple guidelines for CF food consumption; EG2 received refined guidelines for CF food consumption) or a control group (CG) without any intervention. Altogether, the present study clearly demonstrates that simple guideline-provision is a promising and easy-to-use tool for interventions aimed at improving consumers’ ability to identify CF food in daily life.     

基于Android的智能家居控制系统的设计研究

随着科学技术的发展，各种电器和家具控制的智能化是目前市场流行起来。人们生活水平的不断提高使人们对生活的质量有了一定的要求。在人们的日常生活中，家用电器是不可缺少的生活用品，如何快捷方便的对这些电器进行管理，使我们需要进行研究的任务。智能家居控制系统的出现为人们的生活带来了便利，也逐渐的改变和影响着人们的生活方式。本文就基于Android的智能家居控制系统的设计进行分析和研究。     

Selection of features from power theories to compose NILM datasets

The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation.