Electricity Consumption Patterns Research Articles

Analysis of Major Temporary Electrical Equipment Consumption and Usage Patterns in Educational Buildings: Case Study

The energy use patterns of electrical appliances are more difficult to predict than energy use for heating, ventilation, and air-conditioning (HVAC) and lighting, as: (1) there are large varieties of electrical equipment (e.g., appliances, vending machines, etc.) in buildings and each serves a different function; thus, their energy consumption patterns are difficult to predict; (2) electrical appliances are scattered across buildings, most are not permanently fixed to a location, and they consume much energy. Appliances are also not centrally controlled, such as HVAC and lighting. Thus, energy consumption patterns are more difficult to predict. In addition, electrical appliances consume significant amounts of energy to influence energy consumption volatility. This case study focuses on understanding the energy consumption patterns of electrical appliances in educational buildings. This research analyzes the electrical appliances and energy consumption data from institutional buildings and the factors that drive energy consumption. The analyses show that: (1) energy consumption patterns are dependent on building characteristics and use; (2) the number of appliances in a building influences the peak electricity consumption; (3) vending machines and fridges consume significant amounts of electricity; it has been proven (by minimum building energy loads) that buildings that have more vending machines have significantly higher minimum loads than no or fewer vending machines; and (4) the energy-saving potential from desktops and monitors rose to 60 kWh during lunchtime and 500 kWh at night.

Competing water uses between agriculture and energy: Quantifying future climate change impacts for the Portuguese power sector

Climate change may increase water needs for irrigation in southern Europe competing with other water uses, such as hydropower, which may likely be impacted by lower precipitation. Climate change will also potentially affect the variability and availability of other renewable energy resources (solar and wind) and electricity consumption patterns. This work quantifies the effect of competition for water use between irrigation and hydropower in the future 2050 Portuguese carbon-neutral power sector and under Representative Concentration Pathway 8.5 climate change projections. It uses the power system eTIMES_PT model to assess the combined effects of climate change on the cost-optimal configuration of the power sectorconsidering changes in irrigation, hydropower, wind and solar PV availability. eTIMES_PT is a linear optimisation model that satisfies electricity demand at minimal total power system cost. Results show that, by 2050, climate change can lead to an increase in annual irrigation water needs up to 12% in Tagus and 19% in Douro watersheds (from 2005 values), with substantially higher values for spring (up to 84%). Combining these increased water needs with the expected reduction in river runoff can lead to a decline in summer and spring hydropower capacity factors from half to three times below current values. By 2050, concurrent water uses under climate change can reduce hydropower generation by 26–56% less than historically observed, mainly in summer and spring. Higher solar PV, complemented with batteries’ electricity storage, can offset the lower hydropower availability, but this will lead to higher electricity prices. Adequate transboundary water management agreements and reducing water losses in irrigation systems will play a key role in mitigating climate impacts in both agriculture and power sector.

Consumer's perception towards electricity theft: A case study of Islamabad and Rawalpindi using a path analysis

Electricity theft is a chronic issue and energy worth billions of dollars is stolen annually from electricity grids mainly in the developing countries. This study highlights socio-economic and institutional factors deteriorating the electricity theft situation using the path analysis by employing a measurement model in the analysis of a moment structures (AMOS). Preceding the path analysis, statistical package for social sciences (SPSS) is used for descriptive statistics, reliability analysis through Cronbach's alpha test, and exploratory factor analysis through KMO & Barlett's test of sphericity. The study analyses the factors that facilitate the illegal consumption of electricity in the service area of Islamabad Electric Supply Company, Pakistan, utilizing a primary dataset collected through a structured questionnaire from the twin cities of Islamabad and Rawalpindi. The analysis is based on perception of the consumers. The factors that significantly and positively affect the behavior towards theft include corruption diluting deterrence and power outages/load shedding, whereas the age of the respondent negatively affects their perception towards electricity theft. The role of electricity tariff rate, electricity consumption pattern, probability of detection, the rule of law, education of the respondent and quality of conduct of utility officials appeared insignificant in motivating consumers to steal electricity.

Enhanced LSTM-based community energy consumption prediction model leveraging shared building cluster datasets

Unlike in the past, as the scale of buildings expands from a single building to a group of buildings, it is necessary to respond to the demand for energy consumption for the entire community. This study attempted to improve the electricity consumption prediction performance of single buildings by reflecting the inherent occupant behaviour patterns embedded in the shared electricity consumption data of single buildings. The proposed method utilized electricity consumption data for all other 7 target buildings as a part of LSTM model inputs in combination with meteorological data to predict the electricity consumption profile of a specific building. As a result, the proposed method reduced the error by utilizing the similar energy profiles of the surrounding buildings, *and the prediction performance of the total electricity consumption of the community was increased by 5.11% based on RMSE. The newly proposed method reflects the inherent occupant behaviour patterns and temporal electricity consumption patterns from energy profiles of nearby buildings.

Real-Time Data Analytics for Monitoring Electricity Consumption Using IoT Technology

Rising electricity bills as a result of climate variability and new home electrical and electronic appliances are becoming a major source of concern for most end users. Consumers are typically unaware of their household electricity consumption patterns and the costs associated with them, making proper planning and budgeting difficult. Monitoring and controlling energy consumption on appliances can reduce energy costs for end-users. The Internet of Things (IoT) has the potential to provide remote monitoring and control of devices via automated monitoring and control. In this study, we propose an IoT-enabled system for monitoring and controlling energy consumption in homes to save money and make electricity more affordable to low-income people. Autonomous sensor nodes attached to power outlets collect and route power consumption data to the GSM-enabled gateway. Data aggregation is performed by the gateway for data received from all end-nodes within its coverage area. The gateway sends the data to the cloud, where real-time data analytics is performed. We also developed an algorithm that enables users to not only understand their usage patterns but also remotely control their appliances at any time. Mobile phones are used to remotely turn on and off household appliances via a machine-to-machine interface, as well as to provide real-time visualization of household energy consumption patterns.

Development of Medium-Term Water Inflow Forecasting Models for Planning Electricity Generation in Isolated Power Systems

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

Analysis of photovoltaic self-consumption systems for hospitals in southwestern Europe

A proposal is made for the energy modernization of a group of hospitals in south-western Europe, through the installation of photovoltaic self-consumption systems based on their electricity consumption patterns. For this purpose, the energy demand of these hospitals was examined in detail, self-consumption photovoltaic systems were designed with simulation software and the energy and environmental results were studied. The average annual consumption curve of the hospitals favoured the optimal exploitation of the photovoltaic systems, obtaining very high self-consumption rates. Furthermore, by using 30–50% of the roof surface, an average of 25–30% of the annual electricity demand could be covered and potentially reduce CO2 emissions. Thus, the incorporation of photovoltaic systems for self-consumption in hospitals located in a Mediterranean climate is a very interesting solution.

Behavior segmentation of electricity consumption patterns: A cluster analytical approach

Developing effective energy use and management strategies requires the knowledge of determinants and patterns of the electricity usage behavior of different consumers. This paper examines data-driven unsupervised learning schemes to partition the smart meter users into different clusters such that the users in the same cluster have similar consumption patterns represented by their similar daily routines and peak demand time periods. However, it is critical to resolve the problem of high dimensional space in long time series of smart meter data to make the clustering practical. This problem is resolved by time series data mining based on meaningful statistics and behavioral characteristics to emphasize the essential features for reducing the dimensionality and noisiness of the data. Our work involves rigorous data analysis of smart meter data to identify the essential behavioral characteristics and the source of variability in the behavior and analysis of these behavioral characteristics to select the clustering algorithm and its parameters appropriately. These behavioral features will be extracted from each time-series and then used for clustering using a Gaussian mixture modeling​ algorithm. The clustering algorithm is executed on a real-world electricity consumption dataset of 5038 consumers in Slovenia. The classifications obtained with the algorithm are effective when tested visually and by scoring methods of internal indices. The results indicate that the time of day, day of the week, day-to-day variability and seasonality differences that exist in different clusters can be used to formulate new energy management and grid planning strategies.

Impact of COVID-19 crisis on Energy Consumption and socio-spatial behavior of the residential sector in extreme climates. Case study Southwest of Algeria

The outbreak of the COVID-19 pandemic has not only affected the health sector, but has affected all aspects of people's lives, all economic, social and environmental sectors, including global energy. This article aims to study the impact of COVID 19 on the energy consumption of electricity, gas and water in the residential sector, where confinement has presented one of the associated measures taken by many countries to slow down. The objective of this article is to examine both the immediate and long-term impact of Covid-19 on energy consumption and on the socio-spatial behavior of occupants. The results show that there were variations in the domestic spatial spaces and a change in the social practices of each family. There is a sharp drop in the electricity consumption of single-family homes during the pandemic year. Other households have experienced an increase in electricity consumption during the summer months, mainly due to the periods of confinement such as travel restrictions imposed on the one hand and the harshness of the climate on the other. This study is essential for policymakers to detect changing patterns of electricity consumption in the context of emergencies such as the pandemic.

Research on customer’s electricity consumption behavior pattern

The fine-grained customer load data provides a large amount of information for the analysis of customer’s electricity consumption behavior pattern. Therefore, this paper proposes an algorithm for identifying the electricity consumption patterns of electric power users based on k-means algorithm. Our method is verified on the electricity consumption data of Irish residents and Shanghai Southern Industrial and Commercial users. The experimental results show that our method can divide the curves with similar electricity consumption patterns efficiently.

Building an Energy Efficiency System for Residential Households in the Context of Quantitative Feedback

The way in which households consumed electricity largely influences the total amount of energy used. This paper uses questionnaires to obtain information on the daily electricity consumption behavior of 1,140 households. A comprehensive analysis was used to determine the residential electricity consumption pattern. The study showed that, the traditional residential household electricity consumption patten has some effect on the energy saving strategies, however, it did not provide any scientific or rational information loop system overall. The quantitative feedback platform can be used to improve the level of communication among the residents regarding the electric consumption pattern, and to continuously strengthen residents’ awareness of energy saving. In addition, by combining traditional energy saving strategies with the scientific, and systematic residential household electricity energy saving system as a new idea for reducing end-use energy consumption will be useful and may also help in achieving the goal of energy saving subsequently emission reduction.

Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective

This paper mainly investigates the distribution patterns and convergence characteristics of per capita residential electricity consumption (PCREC) in urban and rural areas in China at the provincial level from 2000 to 2017. First, the results show significant positive spatial autocorrelation of PCREC in rural areas. In contrast, it presents insignificant spatial autocorrelation in urban areas. In addition, the distribution pattern of rural PCREC has changed dramatically, while the distribution pattern of urban areas changes more slowly than in rural areas. Second, the sigma convergence trends show that the discrete distribution level of PCREC in rural areas is higher than that in urban areas, and its sigma and absolute beta convergence rates are faster than that of urban areas. Further, the conditional variables for beta convergence indicate that population size, population density, and per capita GDP should be considered for the convergence mechanism of PCREC. In sum, our results reveal the reduction of the inequality of PCREC in China during the study period, especially in rural areas. In addition, convergence modeling indicates that PCREC converges to different equilibrium levels in urban and rural areas, which indicates the need for an urban-rural-tailored set of energy policies in China.

Heterogeneous changes in electricity consumption patterns of residential distributed solar consumers due to battery storage adoption

SummaryThis study provides an empirical assessment of how adopting battery storage units can change the electricity consumption patterns of PV consumers using individual-consumer-level hourly smart meter data in Arizona, United States. We find that on average after adding batteries, PV consumers use more solar electricity to power their houses and send less solar electricity back to the grid. In addition, adding battery storage reduces electricity needed from the grid during system peak hours, helping utilities better flatten the load curves. Most importantly, we find a large degree of heterogeneity in the changes in electricity consumption patterns due to adopting battery storage that are not consistent with engineering or economic principles such as those not maximizing consumers’ economic benefits. Such heterogeneous changes imply that utilities and policymakers need to further study the underlying behavioral reasons in order to maximize the social benefits of battery storage and PV co-adoption.

Federated Clustering for Electricity Consumption Pattern Extraction

The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.

A novel methodology to determine the reactive power range requirements for wind generators considering the correlation of electricity demand and wind generation

Wind farms are an option to provide reactive power while contributing to the control of voltage levels. However, the energy supplied to the power system by wind farms varies according to wind speed. In addition, the electrical system needs more reactive energy support at certain times of the day due to the different electricity consumption patterns of the population. In recent years, several studies have found a correlation between wind generation and electricity demand due to climatic factors. This work presents a new methodology to establish the required values of reactive power from wind turbines to maintain voltage levels within operating standards, considering the correlation between wind generation and electricity demand. The methodology uses the T-Copula transform to correlate the probability distribution curves for the expected values of wind generation and load. The result is a random sample to be inserted into an optimal power flow to determine the transmission systems operating points. From the results of the optimal power flow, a confidence interval is calculated for the injected reactive power values. Such an interval can assist operators in reactive power management one day ahead of operations. The proposed methodology was tested using a 140-bus Spanish existing power network to analyze the values of reactive power injected by wind generators when considering the correlation between generation and demand. The results demonstrate that, when this correlation is considered, the reactive power injection intervals increment more than 0.05 per-unit for some periods and one more system bus. This information can direct operators’ attention to properly define the controllers’ reactive power range at wind turbines’ connection points.

Electricity Consumption using Big Data Analytics

This paper focuses on using big data analytics to analyze the electricity consumption patterns of 500 consumers(residential) in a particular area over 24 months. The data was obtained from MESB and analyzed to understand the trends in consumption better. The analysis was used to create visualizations and graphs that helped to identify the consumption patterns of individual users as well as the overall trends in the area. Using the Power BI tool, a dashboard is created to showcase the results. The dashboard provides valuable information such as the top 10 consumers and average consumption values by user. This information can be used by utility companies to identify areas where consumption is high and to develop strategies to reduce overall electricity consumption. This study demonstrates the value of big data analytics in understanding complex patterns and identifying opportunities for energy savings

Battery Storage at the Secondary Distribution Electricity Grid by Investigating End-Users Load Demand Measurements

Energy storage systems is expected to be utilized to cover the increased electrification of energy demands and to alleviate the electrical energy production from intermittent energy sources such as solar and wind. Aggregated and distributed battery energy storage systems may improve electricity grids operability and security by providing smart energy management options and efficient resources allocation. In this paper, battery storage at the secondary distribution level is explored. The investigation is based on the end-user energy demand behavior. As such, the electrical energy consumption patterns are measured and analyzed in a residential area. Measurements were collected and analyzed in order to record the customers’ behaviors aiming to reveal their differences and similarities. Following this, aggregated and distributed battery energy storage systems are computed based on the features of the measured electrical power consumption patterns aiming to estimate the factors that could potentially incentivize the installation of a battery system either as aggregated at the low voltage transformer side or as distributed system at the load side. The parameters that affect the economic viability of the system are qualitatively evaluated with regard to the profitability of the system.

SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus

To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management.

A robust approach for the decomposition of high-energy-consuming industrial loads with deep learning

The knowledge of the users’ electricity consumption pattern is an important coordinating mechanism between the utility company and the electricity consumers in terms of key decision makings. The load decomposition is therefore crucial to reveal the underlying relationship between the load consumption and its characteristics. However, load decomposition is conventionally performed on the residential and commercial loads, and adequate consideration has not been given to the high-energy-consuming industrial loads leading to inefficient results. This paper thus focuses on the load decomposition of the industrial park loads (IPL). The commonly used parameters in a conventional method are however inapplicable in high-energy-consuming industrial loads. Therefore, a more robust approach is developed comprising a three-algorithm model to achieve this goal on the IPL. First, the improved variational mode decomposition (IVMD) algorithm is introduced to denoise the training data of the IPL and improve its stability. Secondly, the convolutional neural network (CNN) and simple recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and non-invasive decomposition process of the IPL using a double-layer deep learning network based on the IPL characteristics. Specifically, CNN is used to extract the IPL data characteristics while the improved long and short-term memory (LSTM) network, SRU, is adopted to develop the decomposition model and further train the load data. Through the robust decomposition process, the underlying relationship in the load consumption is extracted. The results obtained from the numerical examples show that this approach outperforms the state-of-the-art in the conventional decomposition process.

Spatial and temporal changes in electricity demand regulatory during pandemic periods: The case of COVID-19 in Doha, Qatar

The propagation of the pandemic times, especially during COVID-19, has caused millions of morbidity and mortality cases across the world, forcing people to change their lifestyles and governments to take different measures to slow down the spread of the disease. Consequently, electricity demand and consumption patterns across other socioeconomic sectors were affected by the pandemic. This study aims to investigate the impact of the COVID-19 pandemic on spatiotemporal electricity demand and consumption across six socioeconomic sectors in Doha City, Qatar. The electricity demand and consumption were assessed for three time periods in the pandemic year (pre-lockdown, lockdown, and post-lockdown) compared to electricity consumption prior to the pandemic year (2017–2019). The empirical analysis was performed on a geographically visualized map to determine the areas with high and low electricity consumption. The pattern has been compared before and during the pandemic with previous years. The results show that electricity consumption has increased in the residential and governmental sectors and declined in the industrial and commercial sectors during the lockdown period compared to the post-lockdown period, particularly in the summer season. Mapping the hot/cold spots areas and the temporal analysis of the changing patterns of electricity demand and consumption could provide useful insight to decision-makers for targeted interventions.