Estimation Of Electricity Consumption Research Articles

Accounting and Monitoring System of Electricity Expenditures Based on Geospatient Data Consumers Location

The purpose of the research is considers the development and implementation of a system of accounting and monitoring of electricity costs with visualization of geospatial location data by consumers, which provides technical accounting and analysis of the structure of electricity consumption, estimation of electricity consumption for the tasks of planning production volumes and purchasing electricity from producing companies based on consumer expenditure statistics, prevention of losses associated with unauthorized electricity extraction.Methods. When creating a project and software implementation of a system for monitoring and controlling energy consumption, methods of system analysis, methods of designing software for information systems, a unified object-oriented modeling language Unified Modeling Language (UML), DBMS SQLServer, development environment Microsoft Visual Studio Code (VS Code.) were used.Results. During the project, software for a monitoring and control system for electricity consumption with visualization of geospatial data on the location of consumers was developed and implemented.Тhe project was implemented, tested and introduced into the activities of JSC АtomЕnergoSbyt when planning and accounting for electricity supplies to consumers.Тhe testing results showed that the software system fully satisfies the functional requirements, is ready for use and was demonstrated at the exhibition center.Conclusion. А system for recording energy consumption data with visualization of the territorial location of consumers has been developed in order to control consumption and payments. Тhe developed system will solve the problem of delay in receiving readings from metering devices in the data collection system. Visualization of energy resource con- sumption data by objects and their territorial location with linked personal accounts, meters and consumption volumes will allow you to quickly control the volume of supplies and energy consumers. availability of payment, which will reduce unproductive losses and unauthorized connections.For the company JSC Аtomenergosbyt, operational visualization of consumer data makes it possible to analyze the coverage of consumers by territorial basis and plan new connections.

Development of a methodology for the sampling and treatment of food waste and food residues in Kara Tepe (Lesvos) refugee camp

AbstractThis research focuses on the management of food waste in small communities such as the one at the Reception and Identification Center (RIC) of Kara Tepe in Lesvos (Greece), using the method of home composting. To understand production patterns and possible variations of the characteristics of food residues, a sampling methodology was designed for obtaining representative samples of the whole population of the center. Two composting trials were carried out using household composters with a tailored active aeration system. The first trial contained food residues and olive leaves as a bulking agent (Κ1), and the second one contained food residues, olive leaves and two-phase olive pomace (K2) to investigate co-composting with local agricultural byproducts. Results indicate that the compost produced in the first trial (K1) was stable but phytotoxic and unsuitable for use as a soil amendment. The compost produced in the second trial (K2) was also stable and non-phytotoxic but potentially harmful for very sensitive plants if used as a soil amendment because of high pH values. In addition, an analysis of the operational cost of the proposed treatment process was performed, involving the estimation of electricity and water consumption. The findings indicate that 304 home composters can cover the requirements of a population of this size (approximately 1300 people), at a cost of a mere 3.36 €/day. Given the unique challenges faced by island communities in contrast to urban areas, including differences in strategy development, waste management practices, infrastructure, cultural considerations and innovation potential, the findings serve as a foundational guide for similar island communities in Mediterranean areas (for policy and decision makers, stakeholders, academia, etc.) to develop tailored strategies within the framework of the circular economy that particularly focus on composting for reducing food waste and utilizing byproducts. Therefore, it is feasible to exploit the proposed methodology in similar-sized communities—villages or even a block of flats—for the on-site treatment and utilization of food residues since it can provide a good-quality compost at a reasonable cost.

Cognitive Modelling and Forecasting of Electricity Consumption

Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of the administrative-territorial formation. The approach to the description of factors of electric power consumption by both population and various branches of national economy, as well as organisations engaged in rendering various services has been proposed. Special software has been developed, which allows to obtain model results of electric power consumption in an automated mode, to carry out factor analysis of power consumption. The experimental verification of the work of the programme of cognitive modelling and forecasting of electric power consumption by the population of Lgovsky district of Kursk region is given. The developed software also makes it possible to evaluate the adequacy of the obtained results and promptly adjust the model parameters.Results. As a result of the research a fuzzy cognitive map of energy consumption for a municipal entity was developed. The concepts of the subject area describing the influence of various groups of factors on the level of electric energy consumption were identified. Forecast estimates of electricity consumption were obtained, which were based on the data for the retrospective period. Adequacy indicators based on the calculation of statistical criteria are determined for the obtained estimates.Conclusion. The results of the study have shown that the combination of cognitive and statistical methods allows to achieve an adequate solution when solving the problem of energy consumption forecasting.

Projection of Non-Industrial Electricity Consumption in China’s Pearl River Delta under Global Warming Scenarios

Global warming is a global issue closely linked to sustainability, and power systems around the world are facing immense pressure due to global warming. The purpose of this study is to investigate the impact of global warming on non-industrial electricity consumption in China’s Pearl River Delta. The Weather Research and Forecasting (WRF) model is employed to dynamically downscale and simulate summer climate change characteristics during historical periods and future warming scenarios of 1.5/2 °C. Then, in order to dynamically investigate the changes in non-industrial electricity consumption in cities after warming, we developed a non-industrial electricity consumption estimation model based on degree days and GDP. The regression model can well reproduce non-industrial electricity consumption in summer. Under future warming scenarios of 1.5/2 °C, the results indicate an annual growth trend in non-industrial electricity consumption due to global warming. Under a 1.5 °C warming scenario, non-industrial electricity consumption in both Guangzhou and Zhuhai increases, with Guangzhou experiencing a larger increase of about 10 terawatt-hours (TWh) compared to the historical period. However, under a 2 °C warming scenario, non-industrial electricity consumption in both cities slightly decreases compared to the 1.5 °C warming scenario, with a maximum decrease of 874 million kilowatt-hours.

Estimating electricity consumption at city-level through advanced machine learning methods

An effective energy management system relies on the accurate prediction of electricity consumption, facilitating energy suppliers to optimise energy distribution, reduce energy waste, and avoid overloading the power system. This paper analyses different methods for the estimation of electricity consumption at the level of an urban area. A statistical model based on Trigonometric seasonality, Box-Cox transformation, Auto-Regressive Moving Average errors, Trend and Seasonal components is first presented. Then a model based on fuzzy logic is also proposed. These methods will be optimised and evaluated on a dataset collected by the electric power supply agency of Sibiu, Romania, with the goal of reducing the forecast error. The models are also compared with a Markov stochastic model and with a Long Short-Term Memory neural model. The experiments have shown that our statistical model using a history length of 200 electricity consumption values and a daily seasonality is the most efficient, with the lowest mean absolute error of 3.6 MWh, thus making it a good candidate for integration into a city-level energy management system.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Comparing different approaches of agent-based occupancy modelling for predicting realistic electricity consumption in office buildings

Having a good grasp on modelling the dynamics of occupants for estimating electricity consumption in office buildings is a vital asset for realistic predictions. Nowadays, agent-based models are widely used for this purpose. Previous approaches to modelling dynamics of occupants in multi-floor office buildings simplified the models by teleporting agents between zones during transitions without considering the routes used to reach their final destination such as going through corridors, stairways and hallways, thus, underestimating the potential energy usage during those transition period. This paper proposes a more realistic approach by incorporating detailed routes of agent movement when transiting from one zone to another. To demonstrate the case, detailed routes and route choice preferences are used as inputs within the model for the agents to make independent decisions when transiting from one place to another within the simulated office building. The route choice preferences are computed from data gained from an earlier extensive real world occupancy detection trial conducted within the model office building using state of the art indoor positioning system. The simulation experiments compare the previous approach against the proposed approach and based on the evaluation it is found that there is approximately 19% underestimation of electricity consumption per day when detailed routes are not considered. The research demonstrates, the proposed approach is applicable to any office buildings and will produce predictions which will be much more realistic and closer to the real world electricity consumption level.

Development of Web-Based IoT Comfort Index and Electricity Consumption Estimation Monitoring Microcontroller System with Wireless Sensor Modules for Air-Conditioning System

Nowadays, electricity consumption especially in Malaysia has increased by the year. One of the factors that led to the increase in electricity consumption of a building is the usage of air conditioners (AC). The inability of conventional AC remote controls to monitor indoor environment conditions of the building will lead to unnecessary extreme temperature setting, then causing unnecessary high electrical consumption. Also, the conventional AC remote control is not able to store and perform analysis on the environment condition data of the building for the use of energy management strategy. The conventional AC remote control is only able to show the temperature set point value of the system, which could not represent the comfort experienced by occupants. In this project, a new device which can monitor the indoor environment condition and estimated electricity consumption on a web server is developed. The device monitors a comprehensive thermal comfort index Predicted Mean Vote (PMV) representing the comfort experienced by the occupants. The device is equipped with wireless sensor modules for indoor and outdoor remote monitoring. The device can monitor the outdoor temperature, indoor temperature, and indoor relative humidity along with indoor PMV value and electricity consumption. This device can grant advance feature to conventional AC remote control without causing physical modification to the existing system.

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption

The energy supply sector faces significant challenges, such as the ongoing COVID-19 pandemic and the ongoing conflict in Ukraine, which affect the stability and efficiency of the energy system. In this study, we highlight the importance of electricity pricing and the need for accurate models to estimate electricity consumption and prices, with a focus on Spain. Using hourly data, we implemented various machine learning models, including linear regression, random forest, XGBoost, LSTM, and GRU, to forecast electricity consumption and prices. Our findings have important policy implications. Firstly, our study demonstrates the potential of using advanced analytics to enhance the accuracy of electricity price and consumption forecasts, helping policymakers anticipate changes in energy demand and supply and ensure grid stability. Secondly, we emphasize the importance of having access to high-quality data for electricity demand and price modeling. Finally, we provide insights into the strengths and weaknesses of different machine learning algorithms for electricity price and consumption modeling. Our results show that the LSTM and GRU artificial neural networks are the best models for price and consumption modeling with no significant difference.

RECURRENT ANALYSIS OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE

The subject of the study is the study of models and methods for short-term forecasting of energy consumption in power systems based on recurrent analysis of time series. The aim of the work is a recurrent analysis of the time series of energy consumption of a metallurgical enterprise and the development of a program in the Matlab environment for automating calculations and experimental testing of data available for research in PJSC Electrometallurgical Plant "Dniprospetsstal" named after A. M. Kuzmin. The following tasks have been solved: the methodology for constructing recurrent diagrams and their quantitative analysis have been considered; a model of the time series and the phase trajectory of the time series was built to visualize the change in energy consumption during the day; software for constructing recurrent diagrams in the Matlab package was developed. Methods were used: analysis of time series based on recursive analysis to study the characteristics of the state of the system on the example of a metallurgical enterprise. The results were obtained: software was developed in the Matlab environment for short-term forecasting of energy consumption in power systems, and quantitative indicators were calculated that can be used to characterize the state of the system and analyze energy consumption in the summer and winter seasons. Conclusions: in the course of the study, software for constructing and quantitative analysis of recurrence diagrams in the Matlab package was developed, with the help of which patterns were discovered and information about the properties of the system under study was obtained. Based on the analysis of the average values of quantitative measures in the off-season for 2018–2021, it can be seen that the summer period is characterized by greater predictability, as well as a significantly higher latency indicator, which characterizes the average time when the system can spend in a more or less unchanged state. Confirmed on real data, the benefits of using the recursive analysis method for estimating electricity consumption, as well as more efficient modeling of this process, can lead to an increase in the accuracy of forecasting its future dynamics.

Electricity consumption estimation with differential polynomial and artificial neural networks: Case of Türkiye

Electrical energy is one of the indicators that express the welfare and modernization level of countries. It is a type of energy that needs to be consumed as soon as it is produced and is cumbersome to store. For this reason, the estimation accuracy of the consumption demand is important in order to meet the supply. In this study, Artificial Neural Networks (ANN), which are frequently used in applications, and Differential Polynomial Neural Networks (D-PNN), a new type of neural network, are compared in the electricity consumption estimating problem. While comparing the methods, the independent variable data of exports, imports, population, installed power, and gross domestic product were used as the inputs of the models, and the electricity consumption values of Türkiye in a certain time interval were estimated. As a result of the comparisons, it was seen that the D-PNN method gave good results in performance criteria, ranging from 52.5% to 58.8%, compared to ANN.

Remote sensing of night‐time lights and electricity consumption: A systematic literature review and meta‐analysis

AbstractNight‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations.

An extended approach to appraise electricity distribution and carbon footprint of bitcoin in a smart city.

A nation cannot sustain a highly productive and efficient population without smart cities. Due to their significant reliance on digital technologies, these cities require a high level of cybercrime protection. Cryptocurrencies have gained significant attention due to their secure and reliable infrastructure. The decentralised cryptocurrency operates in a trust-less environment known as the blockchain, where each network participant has a ledger copy of all transactions. Blockchain technology employs a proven consensus mechanism without requiring establishment of a central authority. But the consensus mechanism requires miner to solve a cryptographic problem by generating random hashes until one of them matches the desired one. This procedure is energy-intensive, and when thousands of miners repeat it to verify a single transaction, a substantial amount of electricity is consumed. Moreover, electricity produces a significant amount of carbon footprint. Patch methodology utilises the data of all hashes created per year and the efficiency of mining hardware over a 10-year period to calculate the Bitcoins energy consumption. Due to a large number of unknown and uncertain factors involved, it is difficult to precisely calculate a single value for electricity consumption and carbon footprint as reported by Patch methodology. The proposed method extends the Patch methodology by adding a maximum and minimum limit to the hardware efficiency as well as the sources of power generation, which can help refine estimates of electricity consumption and carbon emissions for a more accurate picture. Using the proposed methodology, it was estimated that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tonnes of carbon dioxide. To address the issue of excessive energy consumption and carbon emissions, a significant number of individual miners and mining pools are relocating to energy-intensive regions, such as aluminium mining sites that rely on hydroelectricity for energy generation.

Application of The Combined Method in Inventory Forecasting Electricity at PT PLN (Persero) ULP Sibuhuan

The forecast of electricity consumption is a prediction of the use of electricity for the future by referring to the use of electricity in past data. Estimates of electricity consumption needs are intended to estimate how much electricity consumption is used by customers and must be provided by PT PLN (PERSERO) ULP SIBUHUAN as a provider of electrical energy services. Population growth occurs along with the development of an area, so it affects the demand for electricity and the need for electricity consumption. Load mapping must be done to maintain the continuity and distribution of electrical energy to customers. One way to preserve the continuity of service is to estimate electric power consumption using several forecasting methods. One of the methods that can be used is to combine several load forecasting methods called the combined method. The combined method is a model that incorporates various estimation methods, including econometrics, analytics, and trends, using historical customer data originating from BPS (Central Statistics Agency) Padang Lawas Regency and PT. PLN (PERSERO) ULP SIBUHUAN. The estimated load between 2021 and 2025 is 625,070,452 kWh, with an increase in electricity consumption of 20.7%, household expenses increasing by 1.8%, and business sector expenses by 1.35%. The method used is to calculate manually using Microsoft Excel to obtain forecasting values for population growth; increasing electricity consumption is very important for planning electric power generation, development, and planning of electric power distribution and mapping of loads on customers.

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

The study of the quality of multi-step time series forecasting

The work is devoted to the study of the quality of multistep forecasting of time series using the electricity consumption data for forecasting. Five models of multistep forecasting have been implemented, with their subsequent training and evaluation of the results obtained. The dataset is an upgraded minute-by-minute measurement of four years of electricity consumption. The dataset has been divided into training, validation, and test samples for training and testing models. The implementation is simplified by using the TensorFlow machine learning library, which allows us to conveniently process and present data; build and train neural networks. The TensorFlow functionality also provides standard metrics used to assess the accuracy of time series forecasting, which made it possible to evaluate the obtained models for forecasting the time series of electricity consumption and highlight the best ofthose considered according to the given indicators. The models are built in such a way that they can be used in studies of the quality of time series forecasting in various areas of human life. The problem of multistep forecasting for twenty fourhours ahead, considered in the paper, has not yet been solved for estimating electricity consumption. Theobtainedforecasting accuracy is comparable to recently published methods for estimating electricity consumption used in other conditions.At the same time, the forecasting accuracy of the constructed models has been improved in comparison with other methods.

Modelagem bottom-up do consumo por uso final de eletricidade no setor residencial no Brasil

Abstract Electricity consumption in the residential sector in Brazil has been increasing annually despite efforts to promote the energy efficiency of household appliances. One of the main goals for achieving more energy efficiency in dwellings is understanding its energy end uses. In this context, this paper presents a bottom-up model developed to analyse regional and national electricity end uses in the residential sector in Brazil based on a recent survey on Ownership of Appliances and Consumption Habits. The percentages of total electricity consumption associated with nine appliances (light bulbs, refrigerators, freezers, televisions, showers, microwaves, washing machines, fans, and air conditioners) were estimated. The values were obtained using the software EnergyPlus for air conditioners and electricity consumption equations for the other eight appliances. Results show that the proposed model gives reasonable estimates of electricity consumption, which were close to the values expected for most appliances. Regionally, the appliances for which ownership and pattern of use are influenced by the climate (electric showers, fans, and air conditioners) obtained the most significant variation in the percentage of electricity consumption.

Short-mid term electricity consumption prediction using non-intrusive attention-augmented deep learning model

Estimates of electricity consumption (EC) can provide effective guidance for energy allocation and energy-saving measures. For improving the accuracy of short-mid term EC forecasting, a novel nonintrusive attention-augmented deep learning model is proposed. This model, which is referred to as NAP-BiLSTM, consists of a nonintrusive attention-augmented-based preprocessing (NAP) block and a regular bidirectional long short-term memory (BiLSTM). The NAP block is a kind of plug-and-play attention mechanism for time series, which can be combined with a deep neural model that processes sequential data without modifying its structure. The effectiveness of the proposed model is verified by two experiments, including univariate prediction using EC data from the U.S. and multivariate prediction using weather and energy data from Valencia. The root mean square error, mean absolute error, and mean absolute percentage error are used as metrics. The results demonstrate that the proposed model is highly promising for both univariate and multivariate analyses and outperforms state-of-the-art deep learning models.

The Use of Regression Method on Simple E for Estimating Electrical Energy Consumption

The continuous increase in population growth has an impact on the electrical energy supply. Based on this increase, electric power producers serve customers using proper forecasts. Therefore, it is a necessity to select the right calculation method with easy implementation. In this study, the population forecasts and economic growth calculations using the GT (Growth Trend) regression method development on Simple E were obtained for the year 2028. Furthermore, electricity consumption estimation was carried out using the DL (Double Log) regression method with growth trend, R, AR, DW, and t values of 6.63%, 0.993, 0.992, 1.21, and 2.18, respectively. The results show that estimated energy consumption was 6.63% annually, with the achievable amount for 2028 being 19,839.83 GWh. Doi: 10.28991/HIJ-SP2022-03-06 Full Text: PDF

Construction and Verification of a Model for Estimating Electricity Consumption of Residential Buildings in a Region for the Development of an Area Energy Demand Simulation Tool

Construction and Verification of a Model for Estimating Electricity Consumption of Residential Buildings in a Region for the Development of an Area Energy Demand Simulation Tool