Electricity Consumption Management Research Articles

Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings

The management of large enterprises influences their efficiency and profitability. One of the important aspects is the appropriate management of electricity consumption used for production and daily operation. The problem becomes more complicated when you need to manage not one but a large complex of buildings with heterogeneous purposes. In the paper, we examine real-time series data of electrical energy consumption in a complex of heterogeneous buildings, including offices and warehouses, using time series analysis methods such as the Holt–Winters model and ARIMA/SARIMA model, and neural networks (Deep Neural Network, Recurrent Neural Network, and Long Short-Term Memory). Experimental research was performed on a dataset obtained from an energy consumption meter placed in the building complex, built in different periods, and equipped with a variety of automation devices. The data were collected over a period of four years 2018–2021 in the form of time series. Results show that classic models are good at predicting energy consumption in the mentioned type of buildings. The ARIMA model gave the best results—for buildings characterized by seasonality and trends the forecasts were almost perfect with actual values.

Standby Power Reduction of Home Appliance by the i-HEMS System Using Supervised Learning Techniques

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user’s lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation.

A smart electricity consumption management framework using internet of things (IoT) to optimize electricity demand

The emerging technologies in the field of the electricity consumption are focusing the strengthening of the infrastructure of the electricity trading utilities. The infrastructure includes the smart devices which can generate real-time parameters for electricity supply monitoring in an efficient manner. This paper proposes an efficient artificial intelligence-based framework for optimal electricity demand load shifting in electricity consumption to minimize power outages in instant grid load fall and reduce the peak-to-average ratio. The framework is analyzing the acquired electricity consumption data for forecasting and load shedding at real-time which can significantly reduce customers' power outage. The proposed framework for optimal electricity demand load shifting is based on the emerging IT/OT (Information Technology/Operational Technology) technologies. We first analyzed the historical consumption of the electricity segment and then developed this framework. The analysis predicts the electricity consumption, can be utilized by the utility end as well as the consumer end by accruing economic benefits through shifting the electricity demand/load away from the very high-priced periods. The model is using various sensors and actuators in IoT Device for collecting the various real-time parameters for the electrical appliances and the machine learning algorithms to optimize the load over the electricity distribution assets. The proposed methodology improves the effectiveness of load optimization across electricity distribution assets by integrating a variety of sensors and actuators in Internet of Things devices to gather real-time metrics from electrical appliances. In addition to helping to forecast patterns in power consumption, the framework's previous consumption data analysis allows utility providers and customers to save money by strategically shifting electricity demand and load away from expensive times.

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

A machine learning framework to assess the impact of the COVID-19 pandemic on electricity consumption patterns

Energy self-sufficiency and resilience are essential elements for long-term urban sustainability. Energy supply in many cities in the United States has been highly dependent on fossil fuels and excessive energy consumption, both at home and outside (i.e., residential and non-residential energy consumption). In this work, we identify electricity consumption spatial trends and profiles across neighborhoods in the city of Chicago using a self-organizing map (SOM) machine learning method as a main tool. Toward this goal, we use an anonymous dataset provided by Commonwealth Edison (ComEd; Chicago's main electricity provider), which includes electricity consumption data of individual residential and non-residential accounts in Chicago. We aggregate electricity consumption to the zip codes level, apply machine learning clustering methods to categorize electricity consumption patterns, and finally couple it with land use variables to find spatial interrelations. The output demonstrates how specific profiles of electricity consumption emerge across zip codes, which is the first step for developing tailored policies to lower energy consumption. The result of this study can help engineers, urban planners, and policymakers develop actionable strategies for electricity consumption management.

An IoT deep learning-based home appliances management and classification system

The rise in household energy consumption globally has increased the necessity for effective electricity consumption management and load monitoring. Smart meters can facilitate fine-grained analysis by providing consumption insights even at the level of individual appliances, for detecting deterioration of appliances, anomalous behavior, and demand response. In this work, we propose a smart home appliance classification that utilizes the deep learning architecture of Long Short-Term Memory (LSTM) trained on the latest version of the Plug-Load Appliance Identification Database (PLAID). The model achieves competitive precision, recall and F1-scores across 16 different home appliances manufactured by 330 vendors. The model is then deployed on a Raspberry Pi micro-controller and interfaced with smart meters in a home to generate almost real-time classification of appliances and transmit this to a cloud database. The results and insights are made accessible to the end user or utility provider through a mobile application connected to the same database.

Digital management of electricity consumption as a factor of transport company value

Transport industry enterprises consume a significant amount of electricity, and management impacts on this process with the help of digital technologies will reduce costs and allow to obtain other beneficial effects. The paper points out that the development of transport infrastructure, electric transport and all categories of smart transport increases the dependence of the efficiency of the functioning of transport companies on the consumed volumes and quality of electric energy. The authors considered the effects from using new methods of digital management of resource consumption, which include increasing the stability of the power system, increasing the accuracy of measuring consumed resources, as well as solving the problems of effective planning and resource allocation. The influence of the effects of digital management of electricity consumption is considered from the standpoint of value-based management. The key conditions for the information support of the functioning of control systems is a change in the format and method of information exchange, i.e. the transition from the transmission of analog data to digitized information blocks. The results of the study are to confirm the positive impact of the use of digital technologies in the management of electricity consumption, primarily on reducing operating costs and, as a result, increasing the value of transport companies, as well as highlighting the prospects for their widespread use in transport systems, which may be the subject of future research.

Control mechanism of electricity consumption in a transport company

To reduce electricity consumption in hierarchical management of fabrication, it is necessary to take into account the activity of managers associated with the presence of their own targets. A three-level model of managing electricity consumption in production is considered. At its top level is the superior who evaluates the executive who is at the middle level. At the bottom of the system is the director of fabrication. The superior must to manage the consumption of the executive in such a way as to save electricity. But the executive knows own electricity-saving opportunities better than the superior. In turn, the director knows his own electricity-saving capabilities better than the executive. So, both the executive and the director can manipulate their electricity consumption to gain more incentives. To avoid this, a control system for electricity consumption management in fabrication is proposed. This system includes procedure for machine learning of the superior and procedure for grading of the executive. Sufficient conditions of the optimality of this system are found, in which random opportunities of decreasing electricity consumption are used. With such a system, the executive is interested in minimizing electricity consumption. The Theorem is proved that for this it is sufficient to use linear procedures of adaptive standardization and stimulation of the director. The proposed approach to control of electricity consumption is illustrated by the example of railcars repairing in the Russian Railways company.

A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid

This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions.

Carbon management strategy effects on the disclosure and efficiency of carbon emissions: A study of Colombian companies’ context and inherent characteristics

The purpose of this study is to identify the contextual characteristics and the company-inherent characteristics to analyze the disclosure of carbon emissions and the efficiency of emissions from the incorporation of a Carbon Management Strategy. This study is based on two models: i) Carbon emissions disclosure and ii) Efficiency of carbon emissions. We develop the models using the relationship between A) the adoption of a Carbon Management Strategy and the disclosure of carbon emissions and B) the impact of having a Carbon Management Strategy and the efficiency of direct and indirect emissions by Colombian companies. We use an Ordinary Least Square regression model for the carbon emissions disclosure model and a robust Ordinary Least Square model on the efficiency model of carbon emissions based on a balanced panel data of Colombian listed companies for the period 2016–2019 and we take sub-samples to analyze scope 1, scope 2 and scope 3. The study includes variables in relation to the companies’ context (fiscal strategy through carbon taxation and geographical location) and company-inherent characteristics (size and sensitive industry sector). The results of the carbon emissions disclosure model suggest that, in large companies, there is a positive relationship between the adoption of a Carbon Management Strategy and the disclosure of emissions for scopes 1 and 2. Sensitive industries disclose the three scope levels. In the regional analysis, the quantitative information on the scope is scarce and suggests that companies are not willing to disclose information that sends a negative signal of their environmental actions to the stakeholders. The results of the carbon emissions efficiency model show an important connection with the indebtedness of companies as a leverage mechanism to acquire technology or manage innovation projects for a more efficient management of Greenhouse Gas emissions. Shareholders also have an impact on efficiency. This is associated with a growing interest in incorporating environmental criteria into their investment decisions and the effect of obtaining information on emissions through voluntary reports. The Carbon Management Strategy index is positive for the efficiency of controlled indirect emissions (scope 2). This finding suggests that emission reduction strategies focus on the management of electricity consumption or the use of renewable sources to improve financial costs and emission indicators. This study is innovative because it is focused on a country without established Greenhouse Gas regulations and it is in a complex scenario where political stability and the rule of law are affected. This study raises a potential topic to explore further because the topic is in a germinal stage in the academic and business field.

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Smart home appliances can take command and act intelligently, making them suitable for implementing optimization techniques. Artificial intelligence (AI) based control of these smart devices enables demand-side management (DSM) of electricity consumption. By integrating human feedback and activity in the decision process, this work proposes a deep Reinforcement Learning (RL) method for managing smart devices to optimize electricity cost and comfort residents. Our contributions are twofold. Firstly, we incorporate human feedback in the objective function of our DSM technique that we name Home Energy Recommendation System (HERS). Secondly, we include human activity data in the RL state definition to enhance the energy optimization performance. We perform comprehensive experimental analyses to compare the proposed deep RL approach with existing approaches that lack the aforementioned critical decision-making features. The proposed model is robust to varying resident activities and preferences and applicable to a broad spectrum of homes with different resident profiles.

BIO-INSPIRED MULTIPLE SCALES PLACE RECOGNITION FOR ELECTRIC SUBSTATIONS

Abstract. We could get many helpful information and results from satellite remote sensing images and aerial images, including disaster monitoring, grid hidden danger identification, and electricity consumption management. In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible. With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources and improving analysing efficiency flexibly, we propose an assessing and verification framework based on bio-inspired perception and understanding, which summarizes the most appropriate image scale in the electric facilities place recognition. The proposed framework consists of different scenes aerial images datasets, several electric facilities place recognition methods, and credible evaluating methods mimicking mammals. Firstly, we gather satellite remote images and aerial images of sufficient electric power facilities in the United States via Google Earth and other public datasets. Then, several typical place recognition methods are adopted to testing recognition ability of multi-scale perception results, like SAD, NetVLAD, and GIST descriptor. To get more reliable result, multi-units and multi-scenes experiments are implemented roundly. After all experiments and evaluations, we could get that the most appropriate image scale is 1000 m size and the highest recognition accuracy of electric power facilities location is 500 m. Conclusion in our article shows the recommended perception form and scale closest to human cognition in the power grid management and maintenance.

A Simulation Model of Power Demand Management by Manufacturing Enterprises under the Conditions of Energy Sector Transformation

This paper addresses electricity consumption management in manufacturing enterprises. The research aims to provide manufacturing enterprises with an effective tool to control electricity costs. Recently, some factors have been observed to affect the rapid changes in the operating conditions of enterprises. These include the transformation of the power sector toward renewable energy, the disruption of supply chains resulting from a coronavirus pandemic, political crises, and process automation. A method for the analysis and management of electricity consumption in enterprises based on simulation modeling is proposed. The simulation model contains predefined objects representing physical system elements and the data processing algorithm. The production order execution time, energy consumption, employee overtime, and machine load are included in the model. The results show that it is possible to determine the level of power available for the process completion and its influence on the production volume and realization time. In the studied case, when the available power was reduced by half, there was an increase in order execution time of nearly 25 percent and an increase in energy consumption of nearly 15 percent. The method can be used in the operational activities of enterprises as well as extended to different types of production processes.

An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application

As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.

A Novel Interactive IoT-Based Smart Electricity Power Consumption Management System

Effective and efficient management of electric power is of significant benefit to the end users and a nation’s economy at large. The unnecessary huge bills and feuds that occur very often in developing countries like Nigeria after every billing period are often because of energy wastage and improper use of energy. This challenge hereby presents us with the need to not only create awareness but to also develop systems that allow for efficient and economic use of electric power. The existing meters that attempt to handle this challenge are in some cases analog, or not interactive, expensive and imposing. These systems are said to be imposing because they do not afford the user the right of deciding what he/she wants to spend in a billing period. Even with the emergence of prepaid meters, users are still unable to interact with individual connection points and decide what is consumed there so as to enhance conservation. These problems have already brought to table the need to develop systems that are automated, yet interactive and smart. The solution is an interactive smart electric consumption management system. Thus, this research work is formed around interaction and smartness. A linPrec Scheduling Algorithm is used to predict what each connection point requires in a billing period by interpolating between previous data points on the system. With the Android App, the User is allowed to communicate with every connection point in the apartment and comfortably determine how much they are willing to spend on electricity in a billing period. The http client guarantees data arrival with a worst-case average response time of 2.095s and a best-case average response time of 0.894s. Also, the power measurement had a Mean Absolute Error of 8.89% which implies high accuracy of 91.1%.

Continuous Delivery of Blockchain Distributed Applications.

Ensuring a production-ready state of the application under development is the imminent feature of the Continuous Delivery (CD) approach. In a blockchain network, nodes communicate and store data in a distributed manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research focusing on continuous practices for blockchain and Distributed Ledger Technology (DLT). Specifically, it lacks such works with support for both design and deployment. The author has proposed a solution that takes into account the continuous delivery of a business application to diverse deployment environments in the DLT network. As a result, two continuous delivery pipelines have been implemented using the Jenkins automation server. The first pipeline prepares a business application whereas the second one generates complete node deployment packages. As a result, the framework ensures the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The Smart Contract Design Pattern has been used when building a business application. The modeling aspect of blockchain network installation has required using Unified Modeling Language (UML) and the UML Profile for Distributed Ledger Deployment. The refined model-to-code transformation generates deployment configurations for nodes. Both the business application and deployment configurations are stored in the GitHub repositories. For the sake of verification, tests have been conducted for the electricity consumption and supply management system designed for prosumers of renewable energy.

Towards Continuous Deployment for Blockchain

Ensuring a production-ready state of the application under development is the immanent feature of the continuous delivery approach. In a blockchain network, nodes communicate, storing data in a decentralized manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research, focusing on continuous practices for blockchain and distributed ledger technology. In particular, such works with support for both software development disciplines of design and deployment. Artifacts from considered disciplines have been placed in the 1 + 5 architectural views model. The approach aims to ensure the continuous deployment of containerized blockchain distributed applications. The solution has been divided into two independent components: Delivery and deployment. They interact through Git distributed version control. Dedicated GitHub repositories should store the business application and deployment configurations for nodes. The delivery component has to ensure the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The deployment component is responsible for providing running distributed applications in containers for all blockchain nodes. The approach uses Jenkins and Kubernetes frameworks. For the sake of verification, preliminary tests have been conducted for the Electricity Consumption and Supply Management blockchain-based system for prosumers of renewable energy.

Electricity consumption analysis and management for different residential buildings in jeddah, saudi arabia

Electricity consumption analysis and management for different residential buildings in jeddah, saudi arabia

Electricity consumption analysis and management for different residential buildings in jeddah, saudi arabia

Electricity consumption analysis and management for different residential buildings in jeddah, saudi arabia