Electricity Consumption Model Research Articles

A hybrid forecasting model for general hospital electricity consumption based on mixed signal decomposition

Current research into electricity consumption forecasting for General Hospital still has considerable scope for further development, particularly in its failure to incorporate hospital-specific energy usage characteristics as input variables. This study explores the impact of the usage frequency of sizeable medical equipment on the electricity demand of general hospitals. It proposes a hybrid forecasting algorithm that integrates the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Variational Mode Decomposition (VMD) for signal decomposition with the Hyperband-LSTM deep learning algorithm to enhance prediction accuracy. ICEEMDAN is employed for preprocessing the power consumption series, while VMD is used for the secondary decomposition of high-frequency signals within the series. The Hyperband Pruner is utilized to efficiently adjust the hyperparameters of the LSTM, which is then used for electricity consumption forecasting. The predictive performance of the developed method is assessed by comparing it with 15 different forecasting models. The results indicate that the proposed method demonstrates superior forecasting performance. Applying the model to a real-case scenario, it has reduced the hospital’s electricity consumption by about 15%, providing a referable energy management solution for other medical institutions.

Development of mathematical models of power consumption at coal plants

The object of research is coal enterprises where powerful electrical equipment is used (underground installations up to 3000 kW, tunneling complexes of 500–1500 kW, technological complexes of 6–10/0.4 kV, etc.). The deficit of generated capacities, caused by growing energy consumption, can be reduced by regulating power consumption modes. The relevance of this issue is determined by the need to conduct a research of the electricity consumption system, to determine the qualitative and quantitative characteristics of electricity consumption using mathematical models. The lack of mathematical models makes it difficult to analyze energy intensity and consumption modes of each technological operation in the overall balance of electricity consumption of coal enterprises. The article considers the structure and classification of the main technological groups of energy consumers, the development of mathematical models for each type of load modes, as well as a generalized model of electricity consumption of coal enterprises, with the use of mathematical apparatus of probability theory and mathematical statistics. As a result of the work, mathematical models of the electricity consumption process for the main technological groups and for models of daily electricity consumption of coal enterprises as a whole were developed, and it was also established that technological objects of electricity consumption are divided into three different values, in terms of power consumption modes: constant, uniform and pulse. For each class of consumers their statistical characteristics were obtained. The work results can be applied for managing power consumption modes of coal enterprises

Modeling Electricity Generation and Consumption in Cameroon

Currently, there is a significant gap between electricity generation and consumption in Cameroon. Research has shown that electricity consumption in the country is estimated to increase by 965.7 GWh in five years, from 2020 to 2024 due to demographic and economic growth. Hence, this study aims to find methods that can be useful in developing strategies to balance the energy supply and demand in the country. This is done by developing models that can predict future electrical power consumption and generation. Correlation analysis and regression analysis were performed by using data obtained from various databases, and related models were developed accordingly. The model parameters were carbon dioxide emissions, electricity consumption per capita, final consumption expenditures, electricity installed capacity, fossil fuel installed capacity, labor force, and GDP. The models' results demonstrated excellent performance coefficients with RMSE of 0.17041, 0.23893, 0.27571, and 0.2465 for hydroelectricity generation, fossil fuel electricity generation, net electricity generation, and net electricity consumption respectively. Also, hydroelectricity generation, net electricity generation, and net electricity consumption models showed very good RRMSE performance indicating that the models can make predictions with only 4.26%, 5.26%, and 5.77% deviation from the mean values of hydroelectricity generation, net electricity generation, and net electricity consumption, respectively.

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

Prediction Model for Electricity Energy Consumption and Pricing Using Xgboosts With L1 Regularization

The increasing global demand for electricity, driven by rapid industrialization, urbanization, and digitalization, necessitates more accurate and efficient predictive models for electricity generation and consumption. Traditional statistical techniques often fall short in incorporating the complex environmental factors influencing energy demand. This study presents an advanced machine learning model, leveraging XGBoost with L1 regularization, to improve the accuracy of electricity consumption and generation predictions. By integrating comprehensive datasets, including economic indicators and weather variables, the model addresses the critical gaps in existing forecasting methods. The research involved rigorous data pre-processing, feature engineering, and model validation using metrics such as RMSE, MAPE, and MAE. The model achieves a RMSE of 2.212 and MAPE of 2.393%, indicating good predictive performance. Effective data cleaning, feature engineering, and the application of PCA contributed to this result. The results demonstrate significant improvements in prediction accuracy, highlighting the potential of the proposed model to enhance energy management practices, optimize grid operations, and support the integration of renewable energy sources. This study provides a robust framework for future research and practical applications, contributing to the advancement of sustainable energy systems and informed policy-making. The findings underscore the importance of integrating diverse data sources and sophisticated machine-learning techniques to meet the growing challenges in the energy sector.

Energy Consumption Outlier Detection with AI Models in Modern Cities: A Case Study from North-Eastern Mexico

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context.

Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly in rapidly developing regions with harsh climates. This study examines the seasonal variation of water and electricity consumption in residential buildings in Doha, Qatar, exploring the interconnectedness of land use/land cover (LULC) and socio-demographic characteristics with household water and electricity consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation and Bootstrap analysis) and advanced geostatistical models, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), to analyze and monitor the spatial and seasonal variations of water and electricity consumption. The methods involved assessing the relationship between land surface temperature (LST), household water-electricity consumption, and analyzing the impact of demographic variables. Key findings indicate significant spatiotemporal variations in consumption influenced by changes in LULC and demographic characteristics such as household size and structure. The findings highlight the need for integrated urban planning and energy policies that consider the impacts of LULC and demographic changes to enhance energy efficiency and sustainability in urban settings. Furthermore, the results underscore the importance of addressing the complex interplay between urban development and resource consumption in policy-making.

A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

A Monitoring Model for Abnormal Electricity Consumption Based on K-Means++ Clustering and Improved K-Nearest Neighbor Algorithm

Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models

Forecasting of electricity consumption is a critical issue, due to its importance in the planning of the energy trading countries. Several new techniques such as hybrid models are used as well as classical single models to estimate electricity consumption. This study aims to get the best electricity consumption model of Türkiye. For this, the forecasting performances of single and hybrid electricity consumption models, SARIMA is the time series model, ANNs and MLPs are machine learning single models and SARIMA-ANNs and SARIMA-MLPs are hybrid models of machine learning, are compared. This study employs new hybrid models and examines whether the multiplicative model of Wang et al. or the combined model of Khashei and Bijari is superior to than Zhang's hybrid model commonly used as the ARIMA-hybrid model with well known flaws. The results show that hybrid models are more accurate than single time series/machine learning models when forecasting Turkish electricity consumption. Moreover, The Khashei and Bijari hybrid model outperformed the other models and it was determined as the best model for forecasting Türkiye's electricity consumption.

A deep learning approach for fairness-based time of use tariff design

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

An integrated system dynamics model of electricity production, consumption, and export policy in Iran considering carbon emissions

The electricity industry is a cornerstone of modern economies, requiring accurate forecasting to facilitate informed decision-making aligned with sustainable development goals. This study employs a novel, comprehensive system dynamics model comprising four subsystems: consumption, production, carbon emissions, and electricity trading. Our analysis reveals significant trends in Iran's electricity industry. The household sector is projected to become the largest energy consumer, with anticipated reductions in consumer sector subsidies over time. By 2040, total electricity production is forecasted to reach 862,835 million kWh, with renewable sources contributing only 3.3 percent. This trajectory is accompanied by a substantial increase in carbon emissions, reaching 448,383 tons, an alarming 2.6-fold surge compared to 2020. The private sector is anticipated to account for 70 percent of these emissions. Given the findings, prioritizing renewable electricity sources becomes imperative to align with sustainable development goals. Utilizing this integrated model, stakeholders can formulate informed strategies and interventions to navigate the evolving landscape of Iran's electricity sector.

СИСТЕМНО-ПАРАМЕТРИЧНЕ ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ ЕЛЕКТРОСПОЖИВАННЯ ЗАЛІЗОРУДНИМИ ПІДПРИЄМСТВАМИ

The components of electricity consumption by iron ore enterprises are generalized and studied. On the basis of systematicity, a mathematical model has been built that allows developing a methodology for assessing the efficiency of electricity consumption under a three-zone tariff grid. The mathematical modeling of electricity consumption by iron ore enterprises as a complex object makes it possible to numerically determine the impact of the value of electricity consumption on the cost of electricity consumption by an iron ore enterprise. This approach made it possible to obtain quantitative indicators of the efficiency of electricity consumption at specific iron ore enterprises. The methodology for assessing the efficiency of electricity consumption at iron ore enterprises was further developed. For the first time, a model for assessing the efficiency of electricity consumption under a three-zone tariff grid was formulated and built. The proposed methodology can be used as a basis for unifying the quantitative assessment of electricity consumption efficiency. The analysis of the results makes it possible to conduct a quantitative and qualitative assessment of the efficiency of electricity consumption processes on the basis of system-parametric modeling for iron ore enterprises. The developed methodology can be unified for implementation at iron ore enterprises of the national economy. It is advisable to include the developed model in the practice of relevant calculations of the economic activity of iron ore enterprises. It has been established that in the current conditions of functioning of iron ore enterprises it is advisable to study the consumption of electricity as a systemic complex object. The carried out analysis made it possible to build an objective function that takes into account the consumption of electricity by ore intake devices depending on the time of day

Stochastic Models of Electricity Consumption

The article considers a stochastic model of electricity consumption based on the convolution theory. It is assumed that the processes of switching on and off energy consumption on a city scale depend on a large number of independent random factors and have the form of a normal probability distribution. The functions of switching on and off the load are presented, graphs are plotted. The comparison of the intelligent power supply system with the existing power supply system in the Russian Federation is carried out. The reasons for the slow introduction of smart grids in the Russian Federation are revealed. At the moment, smart grids are not very popular in the Russian Federation, while in the countries of the European Union, the USA and China, projects related to the development, production of components and the widespread introduction of smart grids are actively receiving state financial support. Thus, the European Union allocates more than a billion dollars annually for projects in the field of electric power and smart grids. These funds are allocated according to the European Green Deal strategy. At the same time, the State Grid Corporation of China has introduced the concept of the "Global Energy Internet", which, in accordance with the government's expectation, will stimulate the smart grid market in the country. As in other countries of the world, the US electric power industry is also facing problems related to rising utility prices, fluctuations in peak load and the need to reduce carbon dioxide emissions. The consequence of this was the adoption at the end of 2021 of an "infrastructure" law providing for large-scale investments in green energy projects.

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.

Analysis of multi-objective cooperative optimization strategy for residential load under extreme weather

In recent years, the problem of seasonal power supply shortages in extreme weather has frequently occurred across China, which has brought great inconvenience to people’s production and life. At the same time, with the advancement of the energy revolution, the proportion of electric energy in the terminal energy is increasing, which also poses a severe challenge to China’s energy security. Our research, by constructing a residential electricity consumption model and conducting multiple electricity price response experiments, has collected data that assesses various factors involved in user responses. It has also explored and inferred the impact of these factors on user-side responses. This study introduces new research directions aimed at alleviating electricity supply-demand imbalances during different periods, contributing to the optimization and enhancement of the national power system.

Energy consumption profiles in buildings

We propose an intelligent data analysis method for modelling and prediction of electricity consumption in buildings. The objective is to enable a building management system to be used for forecasting and detection of abnormal energy use, determining which are the consumption origins and compare them with other buildings using indicators.

Development of Deep Learning for Power Energy Optimization in the Industrial Robot System

This paper established power consumption modeling and motion estimation optimization of industrial robots. We also studied factors affecting the use of electrical energy, such as friction, torque, and electric current. The energy consumption parameters of each coupling can be quantified through the Deep Learning (DL) technique, Scaled Conjugate Gradient (SCG) estimation, or Simulation and experimentation based on the movement posture of a given robot dynamic model to control the robot operation. The robot dynamic model parameters can be identified and expressed in mathematical equations. Electrical energy consumption estimates were analyzed using the SCG technique to compare with the Nonlinear Least Squares (NLS) method using a large dataset of approximately 60,000 samples. The results showed accurate parameter prediction and electrical energy consumption estimation of the robot locomotion pose. The maximum errors in the SCG and NLS methods were 0.89% and 1.54%, respectively. It indicated that the electric energy consumption model using the SCG estimation method is more efficient than the NLS method.

An EKC-based modelling of CO2 emissions, economic growth, electricity consumption and trade openness in Serbia.

Understanding the complex interactions between the economy and the environment is crucial for promoting sustainable development and mitigating the negative impact of human activities on the Planet. The importance of this issue for Serbia is evident as the country strives to balance economic growth and environmental protection to ensure a sustainable and resilient future. Therefore, the main objective of this study is to investigate and model the relationship between CO2 emissions, economic growth, electricity consumption, and trade openness in Serbia. Initially, an Autoregressive Distributed Lag (ARDL) model is used to characterize the Environmental Kuznets Curve (EKC) using data from the period from 1995 to 2019, followed by the construction of a bootstrap logistic regression model to predict environmental quality in Serbia. Long-term estimates of the model confirm an inverted U-shaped relationship, where all three variables exert a statistically significant influence on CO2 emissions. In the short run, however, a causal relationship is only observed between electricity consumption and CO2 emissions. The logistic regression results show that all three factors significantly influence environmental quality. The study proposes policy recommendations for Serbia, such as promoting sustainable economic growth, implementing long-term programs to reduce CO2 emissions, reviewing trade policies to prioritize sustainable practices, and investing in renewable energy sources to reduce emissions.

Multi-Criteria Optimization of Energy and Water Consumption in Fruit- and Vegetable-Processing Plants in Poland

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry’s final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants.

Decomposition Analysis of Regional Electricity Consumption Drivers Considering Carbon Emission Constraints: A Comparison of Guangdong and Yunnan Provinces in China

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal.