Application of Intelligent Technology in Building Electrical Lighting Project

The rapid development of intelligent technology has led to its introduction into the field of electrical lighting in buildings.It provides more technical support for system design,use,and management,and creates a comfortable and safe living environment.The adoption of intelligent technology enables the creation of an intelligent management system,where controllers and sensors are used to adjust the light source within the building,monitor and manage the lighting system in real time,monitor the energy consumption and safety of the system,and achieve the goal of energy saving and emission reduction.This paper briefly discusses the application and significance of intelligent technology in electrical lighting and puts forward design ideas to optimize electrical lighting and measures for lighting system management.

Research and Application of Artificial Intelligence in Electric Guitar Performance

The article explores the application of artificial intelligence technology in electric guitar playing.It delves into the advantages of artificial intelligence and its seamless integration into electric guitar performance.Additionally,it investigates the application of artificial intelligence technology through an intelligent playing robot.The research aims to offer substantial support for the advancement of artificial intelligence in electric guitar performance.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

AN INTELLIGENT DECISION SUPPORT SYSTEM （IDSS） IN THE OPERATION PROCESS OF ELECTRIC FURNACE FOR CLEANING SLAG

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Research and Application of 3G Electrical Safety Job Site Intelligent Monitoring Device

This paper describes the shortcomings and difficulties of power company security construction, such as site management for construction site security monitoring personnel is limited, in recent years , rural power grids and Urban Network alteration Faced with new situation. The use of advanced science and technology and communication terminal in order to better strengthen the means of power construction site safety supervision, improve the level of safety production supervision, design and development of a new electrical safety job site intelligent monitoring devices. The device consists of three parts of the remote wide angle 360 degrees of portable video surveillance equipment and 3G smart terminal equipment and portable battery. Through the application of such a device, professionals can remotely monitor the construction job site safety, diagnose, and effectively improve the security of the electricity sector management and reduce security risks and personnel on-site monitoring costs for improving the security of the entire power industry field operations with significance.

The Principle and Architecture of a Hybrid System of a Neural Network and an Expert System in Intelligent CAD of Electrical Machines

Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.

智能电网（Smart grid. smart electric grid. intelligent grid）

以双向数字科技创建的输电网络，用来传送电力。它可以侦测电力供应者的电力供应状况，与一般家庭用户的电力使用状况，来调整家电用品的耗电量，以此达到节约能源，降低损耗，增强电网可靠性的目的。智能电网雏型是20世纪产生的，由一些中心发电机向大量用户传输电能的电网的简单升级。

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.

Effect of CF and RPP on the Mechanical and Electrical Properties of Smart Aggregate

By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The study shows that Z type aggregate is more sensitive to the feedback of external force than the prism aggregate in the same loading environment, and it indicates that Z type aggregate is more suitable for the research and application of concrete health monitoring. Although the incorporation of RPP would cause the compressive strength of the aggregates and the elastic modulus of hardened cement mortar to reduce slightly within the dosage of RPP by 2.25% because of the polymer film formed in the internal system, this would improve the deformability of the aggregates. In the early loading stage（in the first 60 seconds）, the intelligent concrete specimens implanted with Z type smart aggregate do not show higher sensitivity as expected, although the resistance change rate changes a little bit more, the overall of it is still in balance. Adding RPP could improve the flexibility of smart aggregates exactly, and it plays an active role in prolonging the life of the smart aggregates. By implanting Z type aggregates the damage and failure of the concrete structure could be predicted accurately in this study. The results of this paper will help to promote further research and application of intelligent concrete.

Design and application of electrical fire monitoring system in mining industry

To protect mining areas from electrical fire,it is very important to install electrical fire monitoring system to ensure safety in development of mineral resources and for buildings.In this paper,design for electrical fire monitoring and detection system with optional sensor modules has been proposed.In addition,necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed.The designed electrical fire monitoring and detection system suitable for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors.Monitoring detectors are manufactured by using embedded technology.External shells deployed have superior enclosure performances and explosion-proof properties.It is easy to install and maintain the system.In general,the system has reached,or even exceeded standards specified in national standards for performances and appearances of such devices.Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.

OPTIMUM DESIGN AND SENSITIVITY ANALYSIS OF PIEZOELECTRIC TRUSS STRUCTURES

This paper is concerned with the optimum design and sensitivityanalysis methods of piezo- electric intelligent trusses on thestructural stiffness and free-vibration frequency. On the basis ofthe finite el- ement emthod and taking into account themechanical-electric coupling effect under electric load and me-chanical load, the computational formula of sensitivities ofstructural displacement and free -vibration frequen- cy with respectto the size and shape design variables are proposed.

Intelligent Models of Long-Term Security Correction in Energy Conversion Dispatching

This paper is research on the dispatching models for monthly generation plan which is manage the congestion considering the security constrains of the power grid. Since the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges, The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Hunan Province power grid is introduced to explain the models.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

The Mathematical and Physical Theory of Rational Human Intelligence: Complete Empirical-Digital Properties;Full Electrochemical-Mechanical Model (Part I: Mathematical Foundations)

The design of this paper is to present the first installment of a complete and final theory of rational human intelligence. The theory is mathematical in the strictest possible sense. The mathematics involved is strictly digital—not quantitative in the manner that what is usually thought of as mathematics is quantitative. It is anticipated at this time that the exclusively digital nature of rational human intelligence exhibits four flavors of digitality, apparently no more, and that each flavor will require a lengthy study in its own right. (For more information,please refer to the PDF.)

Discussion on the Integrated Communication System of Local Electric Power Network in China

This paper describes current situation and special requirements of local electric power communication network in China. The paper discusses the disadvantages of SDH and fiber loop carrier system applied to electric power communication network. It presents a new Communication System-IDS-2000 integrated digital communication system. The paper introduces the characteristics and application of IDS-2000 system in the local electric power communication network.

Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Energy Sources Driven Electricity Production: A Global Tactical and Strategical Paradigm

Energy forecasting for electricity productivity is the process of applying statistics with possible Quantum or Classical Computing with help from new innovative techniques offered by artificial intelligence to make predictions about consumption levels.This kind of computation presents corresponding utility costs in both the tactical and strategical or short term and long term.Energy forecasting models take into account historical data,trends,weather inputs,tariff structures,and occupancy schedules in the urban city due to population growth,etc.to make predictions.Additionally,energy forecasting as future paradigm is driven by electricity production demand and it is a cost-effective technique to predict future energy needs,which is a paradigm to achieve demand and supply chain equilibrium based on available energy both renewable and non-renewable sources.