Collection Of Energy Consumption Data Research Articles

Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.

Design and key technology of the energy consumption management system for the liquid cooling data center

AbstractIn view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. Particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

Energy-Saving Operation Strategy for Hotels Considering the Impact of COVID-19 in the Context of Carbon Neutrality

With the advent of the post-epidemic era, the energy consumption characteristics of hotels have changed, which has an important impact on urban energy conservation. In order to contribute to the goal of carbon neutrality, this study discusses the energy-saving operation strategy of hotels considering the impact of the COVID-19 epidemic. Based on the energy consumption characteristics of large public buildings, this paper analyzes the energy consumption distribution and operation characteristics of hotel buildings in detail. By collecting energy consumption data from five typical large hotel buildings in a tourist city in southern China from 2018 to 2022, the impact of COVID-19 on hotel energy consumption and hotel business characteristics was discussed in detail. Combined with the economic development characteristic in the post-epidemic era, this paper explores the energy-saving strategies that hotels can adopt in the context of normalized epidemic prevention and control and obtains the optimal path of low-carbon economic operation of hotel buildings. This study reveals the energy consumption characteristics and energy-saving potential of hotel buildings, and provides enlightenment for hotel management and low-carbon development in the post-epidemic era.

Analysis of Energy Consumption Related to the Use of Air Conditioning in University Buildings in a Tropical Climate

Objective: To evaluate aspects of energy consumption with the use of air conditioners in two teaching units at the Federal University of Bahia (UFBA): the Polytechnic School (POLI) and the Faculty of Architecture (FAU). Method: The method used was to carry out a level I energy audit, with the collection of energy consumption data in the two units and information about the air conditioning equipment for the period between 2013 and 2018. Results and conclusions: average energy consumption indicators were found corresponding to 58 kWh/m²/year at the Polytechnic School and 40 kWh/m²/year at the Faculty of Architecture. As for the representation of the percentage of energy consumption related to the use of air conditioning, 36% were observed in the Polytechnic School and 19% in the Faculty of Architecture. Research Implications: Contributes to sustainable development and managers' decision making based on the analysis of the collected indices, which can serve as a benchmark for other universities with a tropical climate. Originality and Value: Finally, this research becomes original insofar as it contributes to obtaining indices of energy consumption in universities and data on energy consumed with the use of air conditioning.

Design and implementation of energy consumption acquisition device based on ESP32

In recent years, due to the vigorous development of China’s economy and society, the country’s energy use is increasingly high, and the demand is very large. Effectively reducing power consumption while ensuring economic and social development has become a very important issue that the whole Chinese economy and society are concerned about. Based on this, a device energy consumption acquisition device is proposed in this paper. The hardware adopts ESP32 microprocessor as the core controller, and the current type current transformer is used to collect the voltage and current signal data of the device. Arduino software is used to design the acquisition program and output energy consumption data. Finally, the device is tested, and the results show that the device can collect energy consumption data of the equipment.

Application of a Combined Prediction Method Based on Temporal Decomposition and Convolutional Neural Networks for the Prediction of Consumption in Polysilicon Reduction Furnaces

Countries all over the world are making their contribution to the common goal of energy saving and emission reduction. Solar energy is gaining more attention as a renewable energy source. Polysilicon is an important raw material for solar panels and the production of polysilicon is a vital part of the photovoltaic industry. Polysilicon production is a typical process industry enterprise, and its production process is continuous and highly energy intensive. Therefore, it is necessary to forecast and analyze the consumption of polysilicon production plants. To address the problem that it is difficult to predict future consumption based on historical data alone due to the time-series, massive, nonlinear, and complex nature of data in polysilicon workshops. This study proposes a combined workshop energy consumption prediction model based on Bayesian estimation of time-series decomposition and convolutional neural network (TSD-CNN). The method uses a time-series decomposition method to model the periodic and long-term trend components of the raw consumption data and uses a Bayesian estimation algorithm to optimally estimate the model parameters for each component. With the real-time collection of energy consumption data for equipment, the application of that method described above has successfully improved the accuracy of prediction, production management efficiency and safety warning capabilities in enterprises. Furthermore, it helps to provide decision support for energy conservation and consumption reduction studies. To verify the practicality and reliability of the algorithm in practical applications, experiments were conducted with the energy consumption data of the reduction furnace in the polysilicon production plant, and by comparing it with the commonly used regression methods SVM, LSTM and TSD, the results show that the combined prediction method has a greater improvement in the accuracy of energy consumption prediction.

Green building energy consumption data detection method based on Naive Bayesian algorithm

In order to overcome the accuracy of energy consumption data acquisition and the low timeliness of the detection process existing in the traditional detection methods, a green building energy consumption data detection method based on Naive Bayes algorithm is designed in this paper. After collecting energy consumption data, cluster processing is carried out. Then, on the basis of the analysis of Bayesian basic principle, the Naive Bayesian classification model was designed based on the fast clustering calculation results, and the green building energy usage was analysed, and the energy consumption data quota index was designed, and the energy consumption data verification was completed by combining the Naive Bayesian classification model. The experimental results show that the building energy consumption load collected by this method is closer to the actual energy consumption load, and the detection process takes less than 3.5 min, which fully demonstrates the effectiveness of this method.

A Possible Smart Metering System Evolution for Rural and Remote Areas Employing Unmanned Aerial Vehicles and Internet of Things in Smart Grids.

The way of generating and distributing energy throughout the electrical grid to all users is evolving. The concept of Smart Grid (SG) took place to enhance the management of the electrical grid infrastructure and its functionalities from the traditional system to an improved one. To measure the energy consumption of the users is one of these functionalities that, in some countries, has already evolved from a periodical manual consumption reading to a more frequent and automatic one, leading to the concept of Smart Metering (SM). Technology improvement could be applied to the SM systems to allow, on one hand, a more efficient way to collect the energy consumption data of each user, and, on the other hand, a better distribution of the available energy through the infrastructure. Widespread communication solutions based on existing telecommunication infrastructures instead of using ad-hoc ones can be exploited for this purpose. In this paper, we recall the basic elements and the evolution of the SM network architecture focusing on how it could further improve in the near future. We report the main technologies and protocols which can be exploited for the data exchange throughout the infrastructure and the pros and cons of each solution. Finally, we propose an innovative solution as a possible evolution of the SM system. This solution is based on a set of Internet of Things (IoT) communication technologies called Low Power Wide Area Network (LPWAN) which could be employed to improve the performance of the currently used technologies and provide additional functionalities. We also propose the employment of Unmanned Aerial Vehicles (UAVs) to periodically collect energy consumption data, with evident advantages especially if employed in rural and remote areas. We show some preliminary performance results which allow assessing the feasibility of the proposed approach.

RETRACTED: Agglomeration development strategy of telecom industry based on embedded system and data mining

Aggregation is driven by both the size of the concentration and the city itself in the industry (urbanization). Since the new company to follow the existing companies in the same industry continues to cluster in these areas, the localized economy will create a cluster to produce the same product. The crisis of power shortage in the telecommunications industry in the summer of the peak power consumption period reduces the power cost through load management measures based on Neural Network (NN) using FPGA tool. Field of Business Intelligence (BI) is, by performing the direct mining data in the database, applying the power of data mining in a number of the hands of the business line and analysts. Sample of the customer, install the collect energy consumption data that the digital power meter. Effective load management strategies have been proposed; the load management measures’ contents can be summarized to compare the measured data. Result join the show's load management strategy, the telecommunications industry, and are very satisfied. In the margin adjustment and Time of Use (ToU) period, customers of this industry can save a certain amount of electricity.

Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data

Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.

An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods

In recent years, collecting energy consumption data has become easier thanks to the decreasing of smart sensors cost. Moreover, the capacity of data analysis using big data methods like machine learning and artificial intelligence has increased. Such methods are expected to be useful to increase the efficiency of energy systems. In this paper, an innovative approach based on big data analysis to design cogeneration systems is presented. More specifically, this study describes how cluster analysis can be applied to analyse energy consumption data. The aim of the method is to design cogeneration systems that can suit energy demand profiles more efficiently, choosing the correct type of cogeneration technology, operation strategy and, if they are necessary, the size of energy storages. In the first part of the paper, the method based on clustering to perform the analysis of the dataset is described. In the second part, a case study based on a cogeneration plant (a wood industry that requires low temperature heat to dry wood into steam-powered kilns) is analysed. An alternative cogeneration system is designed by means of the proposed method in terms of the choice of the cogeneration technology, the sizing of thermal storage, and the operation strategy of the plant. Thermodynamic and economic benchmarks are defined to evaluate the differences between as-is and alternative scenarios. Results show that the proposed innovative method is useful to design cogeneration systems for industry allowing energy and economic savings.

Convergence analysis of city-level energy intensity in China

Understanding the convergence patterns of energy intensity and the drivers leading to the club convergence are of great significance for local governments to implement targeted policies to improve energy efficiency. With this in mind, we begin with the collection of energy consumption data of 193 Chinese cities at prefecture level or above, then we adopt the log t-test and clustering algorithm to investigate convergence characteristics of energy intensity. Besides, the Ordered Probit model is adopted to investigate the drivers that affect the formulation of convergent club. We identify four convergent clubs among total 193 cities, and these clubs show great differences in energy intensity. Marketization degree, population density, foreign direct investment, resource endowment, and industrial structure are recognized as the drivers of the formation of convergence clubs. This paper adds more evidence to understand the energy intensity gap, we propose that upgrading the industrial structure, exerting economic assemble advantage, enhancing the level of opening up, and improving the marketization level are favorable measures to reduce energy intensity.

Design of Green Building Energy Consumption Data Acquisition System Based on Biological Nano Sensors

The traditional energy consumption data acquisition system for green building has low accuracy. Based on biological nano-sensors, the energy consumption data acquisition system for green building is optimized. The original chip of the data acquisition sensor of AD9280 chip is adopted, the sampling circuit of energy data is designed according to the construction of attenuation circuit, and the rs-485 communication interface electricity meter is connected with the data collector reflecting the building energy consumption to complete the system hardware design. The function of the data collector was analyzed and the collection program was designed. The energy consumption data analysis method was designed after the serial port code was converted to complete the software design of the energy consumption data collection system for green buildings. It is found that the designed system solves the problem of "data missing zero" and can effectively eliminate the fault of communication lines. Moreover, when the data volume is 800 bits, the time of the energy consumption data acquisition system is only 2.5 s, which is significantly lower than the other two collection methods, and the collection efficiency is high.

Achieving privacy-preserving big data aggregation with fault tolerance in smart grid

In a smart grid, a huge amount of data is collected for various applications, such as load monitoring and demand response. These data are used for analyzing the power state and formulating the optimal dispatching strategy. However, these big energy data in terms of volume, velocity and variety raise concern over consumers’ privacy. For instance, in order to optimize energy utilization and support demand response, numerous smart meters are installed at a consumer's home to collect energy consumption data at a fine granularity, but these fine-grained data may contain information on the appliances and thus the consumer's behaviors at home. In this paper, we propose a privacy-preserving data aggregation scheme based on secret sharing with fault tolerance in a smart grid, which ensures that the control center obtains the integrated data without compromising privacy. Meanwhile, we also consider fault tolerance and resistance to differential attack during the data aggregation. Finally, we perform a security analysis and performance evaluation of our scheme in comparison with the other similar schemes. The analysis shows that our scheme can meet the security requirement, and it also shows better performance than other popular methods.

Big data framework for analytics in smart grids

Smart meters are being deployed replacing conventional meters worldwide and to enable automated collection of energy consumption data. However, the massive amounts of data evolving from smart grid meters used for monitoring and control purposes need to be sufficiently managed to increase the efficiency, reliability and sustainability of the smart grid. Interestingly, the nature of smart grids can be considered as a big data challenge that requires advanced informatics techniques and cyber-infrastructure to deal with huge amounts of data and their analytics. For that, this unprecedented smart grid data require an effective platform that takes the smart grid a step forward in the big data era. This paper presents a framework that can be a start for innovative research and take smart grids a step forward. An implementation of the framework on a secure cloud-based platform is presented. Furthermore, the framework has been applied on two scenarios to visualize the energy, for a single-house and a smart grid that contains over 6000 smart meters. The application of the two scenarios to visualize the grid status and enable dynamic demand response, suggests that the framework is feasible in performing further smart grid data analytics.

Simulation-based machine shop operations scheduling system for energy cost reduction

Owing to the ever increasing requirements in sustainability, manufacturing firms are trying to reduce their energy consumption and cost. In this paper, we propose a simulation-based machine shop operations scheduling system for minimizing the energy cost without sacrificing the productivity. The proposed system consists of two major functions: (1) real-time energy consumption monitoring (through power meters, a database server, and mobile applications) and (2) simulation-based machine shop operations scheduling (through a machine shop operations simulator). First, the real-time energy consumption monitoring function is developed to collect energy consumption data and provide real-time energy consumption status monitoring/electrical load abnormality warnings. Second, the simulation-based machine shop operations scheduling function is devised to estimate the energy consumptions and cost of CNC machines. In addition, an additive regression algorithm is developed to formulate energy consumption models for each individual machine as simulation inputs. The proposed system is implemented at a manufacturing company located in Tucson, Arizona state of USA. The experiment results reveal the effectiveness of the proposed system in achieving energy cost savings without sacrificing the productivity under various scenarios of machine shop operations.

Implementation of the new statistics approach on final energy consumption of biomass in household sector in three countries: Croatia, Bosnia and Herzegovina and Macedonia

The aim of this paper is to present an improved way of collection and compilation of data about solid biomass consumption in households in order to improve accuracy of official energy statistics data. The accurate, timely and reliable energy data significantly contribute to the consistency in national energy statistics, energy balance, as well as for many other obligatory reporting procedures which are requested and prescribed by national and international standards.When compiling energy statistics, statistics on renewables, particularly biomass consumption, it is often the most questionable as little or no available official data exists in the country. According to the international standards and definitions, solid biomass covers organic, non-fossil material of biological origin which may be used as fuel for heat production or electricity generation. In households, the most commonly used biomass are fuelwood and wood residues.In the process of compiling national energy statistics, national institutions responsible for official energy statistics usually estimate biomass consumption based on the reports on fuelwood cuts in state forests and official biomass production, although it is known that consumption is much higher. Over the past two decades, Energy Institute Hrvoje Požar worked intensively on the energy consumption data collection and particularly on the development of the tailored-made surveying methods for different final energy consumption sectors, particularly for the household sector. The similar methods were recommended to national statistics institutes in the countries in the region when providing technical assistance in developing energy statistics.This paper describes the survey results and methods applied for the determination of energy consumption in households and particularly biomass consumption in three selected countries: Croatia, Bosnia and Herzegovina and Macedonia, and recommendations for the further improvements.

Research on the data transmission optimization for building energy consumption monitoring system based on fuzzy self-adaptation method

BECMP (building energy consumption monitoring platform) offers powerful help to realize building energy conservation by collecting energy consumption data. A great quantity BECMPs have been established all over the world in recent years. However, packet loss problem begins to appear when the scale of BECMP is large enough and the network congestion happen. This paper presents a fuzzy control transmission methodology in order to solve the problem based on network delay. The proposed method could adjust the data transmission strategy according to the network congestion level by changing packet interval and packet size to adapt the changes of network load, and improve the quality and efficiency of data transmission. Three novel transmission controllers are proposed, VPI-FSaTC (varying packet interval fuzzy self-adaptive transmission controller), VPS-FSaTC (varying packet size fuzzy self-adaptive transmission controller), and VPIS-FSaTC (varying packet interval & size fuzzy self-adaptive transmission controller). The simulation results indicates that, the packet loss ratio decreases 23%, 71% and 79% by VPI-FSaTC, VPS-FSaTC and VPIS-FSaTC, respectively, compared to the default transmission method.

OEEABed-Online Distributed Energy Efficiency Analysis Testbed and Novel Monitoring Approach under Wireless Sensor Network

Energy efficiency analysis system need to be widely deployed to collect energy consumption data in a timely schedule and also the online monitoring integrated with dynamic analysis. Smart metering system have similar functions and can be used with some equipment reformation, but the now exist system can not be used in a plug-and-play manner, which is the main constraint on large scale deployment. In this paper, a novel distributed measuring approach is proposed with only one modification of the incoming line to monitoring the bus voltage. While other sensor nodes that deployed at several monitor point are designed with integrated communication and measurement component, which can be used online without blackout or power line interruption. Standard IEEE 802.15.4 beacon frame payload is extended on the concentrator side, and thus the zero position sequence (ZPS) and selected full sampling in one period is conveyed in the extended frame to assist each distributed measurement and synchronization. To verified the feasibility of the proposed distributed measurement by using wireless sensor network, we established online energy efficiency analysis testbed (OEEABed), experimental results are presented to illustrate the effect of both network performance and signal processing parameters.

Power consumption modeling and energy saving practices of hotel chillers

Hotel buildings consume a significant amount of energy, especially their chiller systems. This study aims to create a model of the power consumption of the hotel chillers, and to identify the practical means to reduce the power usage of chillers. The examined parameters include air-conditioned floor area, guest floor area, gross floor area, number of employees, room occupancy, food cover, outdoor air temperature, window velocity, service type and relative humidity. It is anticipated that the developed modeling equation may provide a reference for hotel engineers to forecast any diagnostic problems and form a benchmarking indicator for comparing chillers’ energy efficiency. A survey of hotels in Shanghai was carried out to collect energy consumption data of chillers during air-conditioned cooling months. Regression analysis indicates that number of staff was a major and statistically accepted factor in explaining the electricity consumption of chiller in hotels. In addition, the paper discusses some means and suggestions in reducing chillers’ power consumption.