Electricity Consumption Trends Research Articles

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Forecasting Electricity Consumption Trends in India Using ARIMA-Based Regression Analysis: A Focus on Historical Lags

The ability to forecast electricity generation is crucial for effective energy management and policy planning. This study investigates the use of historical electricity generation data as a predictor for future generation trends using an autoregressive integrated moving average (ARIMA)-based regression model. Focusing on lagged values of electricity generation, we assess the predictive accuracy and statistical significance of the lagged variable (LAG1) for forecasting. The results indicate a strong positive relationship between past and future electricity generation, with the LAG1 coefficient being statistically significant at the 1% level. The regression model explains 97% of the variation in electricity generation, demonstrating its high utility for future forecasting. This analysis provides valuable insights for energy policymakers and stakeholders in preparing for future electricity demand.

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.

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

A two-stage electricity consumption forecasting method integrated hybrid algorithms and multiple factors

Electricity consumption forecasting provides a reference basis for electricity scheduling, it has become a research hotspot in the field of electric power. The fluctuation of electricity consumption is influenced by multiple factors, which has strong randomness and are strongly disturbed by noise. Single algorithms cannot ensure the stability and accuracy of electricity consumption forecasting. To address this problem, a two-stage framework for electricity consumption forecasting is proposed in this paper. In the first stage, multi-forecasting algorithms are considered to eliminate noise and forecast the electricity consumption trend. A hybrid model is constructed, which consists of three parts: Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Unit (GRU) and Gradient Boosting Regression Tree (GBRT). The EEMD method is employed to decompose the electricity consumption signal into different frequency components. GRU and GBRT are adopted to forecast high-frequency and low-frequency components respectively. In the second stage, multidimensional meteorological factors are considered to further improve the accuracy of the electricity consumption forecasting. A structure for feature extraction is constructed to reduce the dimension of meteorological factors. The experimental results indicate that the two-stage framework model outperforms mainstream measurement models by an average reduction of 35.7% and 43.2% in MAE and RMSE.

Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target

AbstractThe emergence of novel factors, such as the energy Internet and electricity supply‐side reform within the context of the dual‐carbon target (carbon peaking and carbon neutrality), has heightened the uncertainty surrounding electricity consumption (EC). This increased uncertainty poses challenges for accurate long‐term EC forecasting. Due to the complexities of feature extraction and the absence of labelled data, conventional supervised learning‐based forecasting methods, such as support vector machines (SVM) and long short‐term memory networks (LSTM), struggle to predict EC with precision in situations of heightened uncertainty resulting from the interplay of multiple factors. To address this issue, a novel method based on a conditional generative adversarial network (CGAN) is proposed. Initially, the dominant factors influencing future electricity consumption trends through grey correlation degree analysis and the K‐L information method are identified. Subsequently, an EC forecast model is introduced based on CGAN, adept at capturing essential factors and the non‐linear relationship between EC and exogenous factors. This approach effectively models the uncertainty of EC, accurately approximating the true distribution with only a small dataset. Finally, the proposed method by forecasting China's EC from 2015 to 2020 is validated. The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.

Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov

Northeast China has been facing a severe power shortage situation. Since September 2021, “power rationing” events occurring in many places in the three provinces of northeast China have been causing inconvenience to people’s production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, and the pressure on coal and electricity will further increase. In Heilongjiang Province, due to coal capacity control, limited production led to the high price of thermal coal; wind power photovoltaic output fluctuations, the epidemic, and other reasons also resulted in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of people’s daily electricity consumption, rational distribution of power generation plans, and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of the backpropagation (BP) neural network prediction model so that the final prediction results have the advantages of the BP neural network prediction model and Markov model. In addition, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately.

Application of grey system model with intelligent parameters in predicting regional electricity consumption

PurposeGiven the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.Design/methodology/approachFirst, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.FindingsThe study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.Originality/valueAcknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Organizational level energy conservation and efficiency - a case of public sector organization in Karachi, Pakistan

Pakistan is facing chronic energy crises since many years. This paper aims to provide an overview of the current status of energy efficiency and conservation practices at organizational level in Pakistan. The first phase of this study evaluates the trends in electricity consumption in selected buildings over the last six years (2017–2022). Subsequently, a survey was conducted in selected buildings to assess employee morale and knowledge of energy efficiency and conservation. Results show an insignificant change in electricity consumption in selected buildings during the past six year’s period indicating a poor culture of energy savings in the selected organization. Despite a constant electricity consumption from 2017 to 2022, a significant fluctuation in average billing amounts was observed. This is possibly due to electricity tariff that has continuously been fluctuating since last few years in Pakistan including Karachi. Study reveals that more than 50% of the employees in selected organization were aware about the importance of energy efficiency and conservation however, they rarely practice the energy saving measures during their routine activities. This is perhaps due to the lack of sense of national stewardship among the employees. It was also found that most of the employees of the selected organization are aware and motivated towards the environmental and climate change concerns related to energy production and its efficient use. However, again a small number of employees were found practicing the energy conservation and efficiency measures. According to this study, companies should encourage staff members to save energy by offering frequent trainings and educational opportunities. Additionally, the organizations ought to think about switching to renewable energy sources.

Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption.

Multi-objective carbon neutrality optimization and G1-EW-TOPSIS assessment for renewable energy transition

Successful low-carbon energy systems rely on good economic, environmental and energy efficiency indicators. Yet, the current energy structure has shortcomings in these aspects, hence the need to develop the renewable energy transition. This study proposes a three-phase dynamic energy planning framework based on multi-objective optimization, assessment and analysis. The optimization phase utilizes the NSGA-II to determine the optimal allocation of multiple energy sources by considering various variables and constraints. A comprehensive assessment system combining the G1 method and the entropy weight method is proposed, and the TOPSIS method is used to evaluate the rationality and superiority of the energy structure. The relative trends in electricity consumption and renewable energy penetration are also analyzed in order to predict CO2 emissions. The dynamic framework and comprehensive assessment are applicable to Guangdong Province. Results reveal that Guangdong province should prioritize the development of offshore wind power, ensure the stability of the power grid by steadily developing hydropower and nuclear power, and vigorously develop solar power at the end of the planning period. Guangdong Province can reach carbon peaking around 2028 and net zero carbon emissions by 2060, leading to carbon neutrality. The optimized dynamic energy planning framework can provide effective decision support to promote the transformation of regional energy structures.

Energy Loss Analysis of Distributed Rooftop Photovoltaics in Industrial Parks

The analysis of the loss of distributed photovoltaic power generation systems involves the interests of energy users, energy-saving service companies, and power grid companies, so it has always been the focus of the industry and society in some manner or another. However, the related analysis for an actual case that considers different cooperative corporations’ benefits is lacking in the presently available literature. This paper takes the distributed rooftop photovoltaic power generation project in an industrial park as the object, studies the analysis and calculation methods of line loss and transformer loss, analyzes the change of transformer loss under different temperatures and different load rates, and compares the data and trend of electricity consumption and power generation in industrial parks before and after the photovoltaic operation. This paper explores and practices the analysis method of the operating loss of distributed photovoltaic power generation and provides an essential reference for the benefit analysis and investment cost estimation of distributed photovoltaic power generation systems in industrial parks. The analyzed results reveal that the change loss is stable after the photovoltaic is connected, and there is no additional transformer loss. And before and after the photovoltaic system installation, there was no significant change in the total monthly data difference between the total meter and the sub-meter.

An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with SARIMA time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict the electricity demand in the years 2019 and 2020, which are particularly challenging due to atypical electricity consumption attributed to COVID-19. The results show that the proposed model is capable of following the trend of electricity demand, adapting itself to sudden changes and obtaining an average error of 2.5% which is lower than the average error of 5.43% when using a non-adaptive approach (more than 50% or error improvement).

Patterns and influencing factors analysis of electricity consumption in university dormitories based on balance point temperatures

The high energy density of university dormitories deserves attention for its huge energy-saving potential. To investigate the patterns and influencing factors of electricity consumption in university dormitories, this study presents a methodology that combines clustering and covariance method based on balancing point temperatures. The dataset of 1170 dormitories from a university in China's hot summer-warm winter region are analyzed. The main results are as follows. (1) Using the Joinpoint Regression (JPR) method, two balance point temperatures, 20.1 °C and 26.6 °C, that affect electricity consumption trend are identified. (2) Through cluster analysis of 3D data, the dormitories are grouped into three electricity use patterns. The phenomenon of the shift in electricity consumption patterns from the fall semester to the spring semester is discovered. (3) The impact of 8 non-behavioral factors on electricity consumption is investigated through covariance analysis. The results show that gender is the most influential factor in base electricity consumption, while three factors have the greatest impact on cooling electricity consumption: floor, occupancy and room location. (4) Based on the main influential factors of room cooling load, a hot season daily cooling electricity subsidy strategy is proposed to balance the difference in cooling electricity bills for different rooms. This study provides reliable information to support the design of potential electricity saving and management measures for dormitory buildings.

Optimized multivariate grey forecasting model for predicting electricity consumption: A China study

The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction.

Deriving electricity consumption patterns using a decomposition approach

Studying the effectiveness of certain policies on load management requires detailed knowledge about the consumption pattern of end-use sectors. The consumption pattern includes both short-time scale variations and long-term trends in electricity consumption. This paper proposes a decomposition method to extract the seasonal (spring, summer cooling, autumn, and winter heating), day-of-week (working day, weekend), and time-of-day load curves of end-use sectors from aggregate load data of the electricity distribution network. The obtained data is used to study variations in the consumption patterns of end-use sectors such as households. The estimated load profiles represent the behavior of a large group of consumers for several years. The model is used to disaggregate the load data of Tehran province in Iran from 2006 to 2018. Resultsshow that during years, industrial power consumption has decreased during peak hours at night due to the shrinkage of two-shift industries. By contrast, the households’ load profiles in the autumn and spring load have shifted upward. This is the net effect of reduced lighting electricity consumption and increased access to electricity-consuming devices. Regression analysis on load decomposition results shows a mild effect of energy subsidy reform on lighting and cooling demand.

A comparative analysis of the carbon dioxide emissions-energy profile in Kuwait: status quo versus 2030

Kuwait is among the leading countries globally in terms of its per capita consumption of electricity and water based on the energy outlook report released in 2019. In Kuwait, the CO2 (carbon dioxide) emissions rates have increased from the energy sector due to the burning of significant amounts of fossil fuels to meet the demands of electricity generation and water supply. Under these circumstances, the demanding analysis methodology to forecasting CO2 emissions from the energy sector, the per capita energy consumption and CO2 emissions were 8.9 toe per capita and 21.1 tonnes of CO2-eq emissions per capita, respectively. This paper aims to quantify a comparative analysis model and estimate an numeric magnitude for current and forecasting 2030 CO2 production totals from Kuwait's energy sector and its impact on average atmospheric temperature and energy production. The aggregate carbon intensity (ACI) was used as an indicator to evaluate the current energy situation and predict a model for Kuwait's CO2 emissions situation and identify how the energy demand and supply might evolve by 2030. The CO2 emissions for 2030 and the electricity consumption trend were assumed to be the 'business-as-usual' model using 2nd set of Fuel Analysis USEPA, with five fuel blend scenarios used as the energy sector's predicted fuel blends. The results found that the total CO2 emissions from the energy sector and the ACI of Kuwait in 2016 were 48.6 MtCO2 and 0.69 kgCO2/kWhr, respectively. The 2030 estimations indicated that using an 80% natural gas – 20% gas oil fuel blend in energy production resulted in CO2 emissions of approximately 66 MtCO2, with an ACI of 0.45 kgCO2/kW-hr. The CO2 offset from upcoming renewable energy projects is projected to be approximately 13%.

Research on repair method of abnormal energy consumption data of lighting and plug based on similar features

The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the kNN algorithm, this paper proposes a kNN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the kNN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of kNN-BS is less than 4% for working days and less than 6% for non-working days. Compared with kNN, the average CVRMSE of the kNN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The kNN-BS algorithm shows excellent stability and robustness compared with kNN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The kNN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs.

A Critical Analysis of the Impact of Pandemic on China's Electricity Usage Patterns and the Global Development of Renewable Energy.

The COVID-19 pandemic has impacted economic activity in numerous sectors due to multiple forms of disruption, including border closures, a stay-at-home policy, and social isolation; the electricity consumption trends in this region will undoubtedly improve. This article examines the impact of COVID-19 on electricity generation and consumption in China during the first two quarters (Q1–Q2) of 2020 and 2021. Furthermore, several governments’ perspectives on COVID-19’s implications for renewable energy development, notably offshore wind power and solar photovoltaics (PV), were examined. Results of this article show that COVID-19 impacts the power industry. According to the analysis, during the first two quarters of 2020, the amount of electricity generated and consumed by China decreased by 1.4 and 1.3 percent, respectively, the capacity of the power plants increased by 5.3 GW and coal consumption dropped by 3.6 g/kWh. Investments in the power generation sector increased by 51.5 billion yuan and investment in the power grid grew by 0.7 billion. Additionally, new generation capacity decreased by 378 GW during the first two quarters of 2020. During the first two quarters of 2021, electricity consumption and production grew by 13.7 and 16.2 percent, respectively. Power plants’ capacity increased by 9.5 GW, while coal consumption for power supply fell by 0.8 g/kWh. The investment in power generation projects increased by 8.9 billion, while investment in power grid projects increased by 4.7 billion. Compared to last year’s same period, 14.92 GW of new capacity was installed. Due to lockdown measures, such as studying at home or working at home, domestic power use in the first two quarters of 2020–2021 increased by 6.6 and 4.5 percent, respectively. To minimize COVID-19’s impact on renewable energy development and assist in building offshore wind power plants, economic and financial measures have been put in place to reduce the epidemic’s effect on solar PV systems.

Statistical Analysis of Trends in Electricity Consumption with Reference to Uttarakhand

Electricity consumption indicates countries growth and development. The aim of the paper is to study the trend of per capita consumption of electricity in Uttarakhand during the years 2011 to 2019. The regression techniques with linear regression, quadratic regression and exponential regression were used to carry analysis and to examine trends between number of years and per capita consumption of electricity in kwh (kilowatt hours). The present study suggests the best fit model by comparing R square, adjusted R square and residual means square error (RMSE). The finding suggests that the quadratic regression model is the best fit model for per capita consumption of electricity with R square (coefficient of determination) of 0.95 for forecasting of electricity consumption per capita in Uttarakhand. This will support policy makers and related sectors in order to meet the growing demand of electricity consumption of Uttarakhand.