Electricity Consumption Profiles Research Articles

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

Data Imputation in Electricity Consumption Profiles through Shape Modeling with Autoencoders

Data Imputation in Electricity Consumption Profiles through Shape Modeling with Autoencoders

Electricity Consumption Profile of Urban Households in Nsukka, Enugu State

In recent times, the uncontrollably rising global energy consumption has become a global problem with its attendant socio-economic and environmental consequences. Hence, this study utilized linear regression model to examine how households’ socio-economic attributes and use of appliances together determine electricity consumption and savings in Nsukka metropolis, Enugu state, Nigeria. Socio-economic variables found to have positive and significant effect on electricity consumption were age of working-class respondents, being married, household size, household active members, unemployed household members, prepaid and estimated electricity billing system, monthly income and high electricity supply hours. In terms of electrical appliances, high hours of electronics’ use per week, incandescent and LED bulbs usage, high number of bulbs, cooling hours of 9 and above per week, lighting hours of 16 and above and weekly heating hours were positively related to electricity consumption and statistically significant. We suggest that the creation of energy efficiency awareness at the household and educational institution levels should be vigorously pursued. Government should subsidize the prices of energy efficient appliances to encourage purchases by low-income earners. Constant overbilling of estimated and postpaid electricity users, as lamented by respondents in the survey, should be discouraged by electricity users. This will enable users to conserve energy and not waste energy as revenge tactics for overbilling. The promotion and full deployment of prepaid meters in the study area by electricity providers would go a long way in complementing the efforts for clean development mechanism in Nigeria.

Changes in hourly electricity consumption profiles and price elasticities in Denmark 2019–2022

Changes in consumption profiles affect the need for investments in production and grid capacities. Extending a model with categorical variables for hours of the day, months and types of days to include short-term electricity prices, and applying Danish data for the hourly electricity consumption by aggregated categories of customers, consumption profiles for each of the years from 2019 to 2022 are estimated and it is tested if the profiles change significantly. Further, the short-term price elasticity of hourly electricity demand is estimated. During the first COVID-19 lockdown, residential electricity consumption increased and non-residential consumption decreased and the residential consumption profile on weekdays resembles the weekend profile. However, long-term effects are quite limited. For 2022, the residential consumption profile is significantly different from the profile in 2019. The profile has become flatter, increasing consumption during the night and reducing consumption during the daytime. An increasing number of electric vehicles and individual heat pumps contribute to this change. The short-term price elasticity for total electricity consumption increased in 2022. Especially, the price elasticity for residential customers increased and became significant in the second half of 2022 where electricity prices were high and volatile and customers were aware of prices.

Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

Performance of vertically mounted bifacial photovoltaics under the physical influence of low-rise residential environment in high-latitude locations

This study focuses on finding suitable installation sites for vertical bifacial photovoltaic (VBPV) panels in urban low-rise neighborhoods at high latitudes. The power production of east-west-oriented VBPV systems matches well with domestic electricity consumption profiles, increasing the self-consumption of PV electricity. Furthermore, PV electricity adds economic value by avoiding transmission fees and taxes. These systems are especially beneficial in high-latitude locations characterized by a low solar elevation angle. However, these low angles expose VBPV panels to a high risk of shading losses from their surroundings, and it is unknown how much shading limits the number of suitable installation sites. Here, environmental shading on VBPV panels is quantified for three low-rise residential neighborhoods in Helsinki, a high-latitude location, by comparing the specific yields (annual electricity production per kilowatt peak) of VBPV and monofacial PV (MPV) systems. The results showed that unshaded VBPV systems have a higher specific yield than their MPV counterparts. However, in densely built neighborhoods with tall trees, the lack of suitable installation sites for VBPV panels severely limits the peak power of these systems. Roof ridge VBPVs usually yield high production, while façade- and ground-mounted systems lose between 30% and 70% compared to roof ridge VBPV systems depending on their installation locations. South-oriented MPVs perform better than VBPVs on north-south-facing roofs, both in terms of specific yield and total annual production. Conversely, VBPVs installed on the ridges of unshaded roofs aligned closely with the north-south axis outperform MPVs on east- and west-facing roofs by 20%–30%.

Energy Household Forecast with ANN for Demand Response and Demand Side Management

This paper presents a short term load forecasting with artificial neural networks. Despite the great imprevisibility, it is possible to forecast the electricity consumption of a household with some accuracy, similarly to that the electricity utilities can do to an agglomerate of households. Nowadays, in an existing electric grid, it is important to understand and forecast household daily or hourly consumption with a reliable model for electric energy consumption and load profile. Demand response programs required this information to adequate the profile of energy load diagram to generation. In the short term load forecasting model, artificial neural networks were used, with a consumption records database. The results show that the artificial neural networks approach provides a reliable model for forecasting household electric energy consumption and load profile. To do so and using smart devices such as cyber-physical systems monitoring, gathering and computing in real time a database with weekdays and weekend, can improve forecasts results for the next hours, a strong tool for Demand Response and Demand Side Management.

Improved Anomaly Detection and Localization Using Whitening-Enhanced Autoencoders

Anomaly detection is of considerable significance in engineering applications, such as the monitoring and control of large-scale energy systems. This paper investigates the ability to accurately detect and localize the source of anomalies, using an autoencoder neural network-based detector. Correlations between residuals are identified as a source of misclassifications, and whitening transformations that decorrelate input features and/or residuals are analyzed as a potential solution. For two use cases, regarding spatially distributed wind power generation and temporal profiles of electricity consumption, the performance of various data processing combinations was quantified. Whitening of the input data was found to be most beneficial for accurate detection, with a slight benefit for the combined whitening of inputs and residuals. For localization of anomalies, whitening of residuals was preferred, and the best performance was obtained using standardization of the input data and whitening of the residuals using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ZCA</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ZCA-cor</i> whitening matrix with a small additional offset.

Data-driven model for real-time evaluating capacity of commercial buildings to participate in the demand response

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.

Enhancing anomaly detection in electrical consumption profiles through computational intelligence

The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The "zero energy buildings" idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its "Z" value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.

A novel clustering based method for characterizing household electricity consumption profiles

A new methodology based on expert knowledge and data mining is proposed to obtain data-driven models that characterize household consumption profiles. These profiles are useful for electricity marketers to understand their customers’ consumption. They could then adjust their electricity purchases in the market and provide recommendations to their customers to manage their consumption. The novelty of this research work is proposing a new procedure to determine an adequate number of clusters for a clustering task. Therefore, the proposed new methodology includes this novel procedure to build the models in two phases. In the first phase, clustering algorithms are used to group the data using different numbers of clusters. For the second phase, a new procedure (k-ISAC_TLP) is proposed and used to select the most appropriate number of clusters. This methodology allows the inclusion of domain information. In the case of household electricity consumption, where only groups with a significant number are relevant as long as the error is small, it allows the use of metrics like the mean absolute error and the number of observations (daily electricity consumption profiles). According to experts, the results achieved in two real datasets (from Spain and Ireland), with millions of observations support the methodology and reveal novel knowledge. In both cases, two and a half million observations have been analyzed and around twenty electricity consumption profiles have been detected. The methodology is easily extensible to problems of any domain where clustering algorithms are applicable. A software solution has been implemented and made freely available.

An Analysis of Energy Consumption in Railway Signal Boxes

This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.

Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities (RECs) involves maximizing the self-consumption within a community, particularly in regulatory contexts in which shared energy is incentivized. In many countries, the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users (e.g., residential and commercial users) makes the design of a new energy community a challenging task. This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data (i.e., billed energy), with the aim of estimating the energy shared by RECs. The proposed methodology involves three phases: first, identifying the typical load patterns of residential users through k-Means clustering, then implementing a Random Forest algorithm, based on monthly energy bills, to identify typical load patterns and, finally, reconstructing the hourly electrical load profile through a data-driven rescaling procedure. The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant. The Normalized Mean Absolute Error (NMAE) and the Normalized Root Mean Squared Error (NRMSE) were evaluated over an entire year and whenever the energy was shared within the REC. The Relative Absolute Error was also measured when estimating the shared energy at both a monthly (MRAE) and at an annual basis. (RAE). A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04 %, an NRMSE of 26.17 %, and errors of 18.34 % and 23.87 % during shared energy timeframes, respectively. Furthermore, our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31 % and 0.12 %, respectively.

Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions

The new power sector scenario has focused on integrating renewable energy sources into the grid and achieving trustworthy consumer-utility-stakeholder relationships using smart meters. Granular consumption data obtained from the data management systems of Advanced Metering Infrastructure helps power utilities build an individual consumer's electricity consumption profile that facilitates better load forecasting, efficient management of demand and supply, and more reliable service. However, different challenges concerned with accessibility of data through different communication layers, such as breaches of privacy of consumer data and security of grid have emerged. Due to this, many customers lose faith in the smart grid. To utilize the full potential of the smart grid system these issues need to be addressed to attain a secure, stable, and reliable grid that brings profit to the utility. This paper provides a comprehensive literature survey to achieve the above-mentioned purposes using data analytics. It also addresses different challenges of using data analytics methods and machine learning applications in mitigating these issues. This study would enable the electric utilities to structure a reliable grid to prevent revenue loss and aid the researchers to recognize the potential and challenges of machine learning techniques in the field of the power sector.

Increase of energy self-consumption in hybrid RES installations 1 with PV panels and air-source heat pumps

In recent years, European countries have experienced a noteworthy surge in the interest surrounding renewable energy sources, particularly the integration of photovoltaic (PV) panels with various types of heat pumps. This study aims to evaluate the energy performance of a grid19 connected hybrid installation, combining a PV array with an air-source heat pump (AHP), for domestic hot water preparation in a residential building located in Cracow, Poland. The primary focus of this evaluation is to assess the extent to which self-consumption (SC) of energy can be increased. The study utilizes Transient System Simulation Tool 18 software to construct and simulate various system models under different scenarios. These scenarios include building electricity consumption profiles, PV power systems, and the specified management of AHP. Analyses were conducted over a period of 1 year to assess the operational performance of the systems. In the considered installations, the differences in SC values between PV installation ranged from 9 to 25%. Notably, the highest SC values were observed during the winter months. AHP with operation control allows to obtain in some months of the year up to 35% higher value the SC parameter compared to systems without AHP. The highest annual 29 SC value recorded reached 83.9%. These findings highlight the crucial role of selecting an appropriate PV system size to maximize the SC parameter.

Occupancy-based one-year-ahead heating, ventilation, and air-conditioning electricity consumption optimization using machine learning

The world is increasingly urbanized. While, more than a half of its population lives in big cities, buildings are responsible for nearly the 40 % of the global annual energy consumption. Recent advances in smart grids and the high penetration of renewable energy sources have increased the need for long-term buildings' electricity consumption prediction to support operational decision making, demand response, and the installation of distributed generation systems. In addition, to meet international environmental goals, electric utilities usually offer financial incentives to reduce consumption levels. Heating, ventilation, and air-conditioning (HVAC) systems use the half of the buildings' energy to maintain users' comfort. In this scenario, predicting and optimizing their long-term electricity consumption without neglecting comfort conditions become vital to negotiate beneficious service-level agreements (SLAs) within the framework of demand-side management strategies. In this paper, we predict and optimize the one-year-ahead HVAC electricity consumption profile of a reference office building based on a machine learning approach. Although the occupants' number influences the HVAC electricity consumption, there has long been a lack of high-resolution, non-Boolean, long-term occupancy estimators. In this context, we first forecast the one-year-ahead occupants' number schedule and then use it, together with traditionally used historical weather data, to predict the one-year-ahead HVAC electricity consumption. The consumption profile is then optimized based on the estimated one-year-ahead occupants’ count schedule. The best results, obtained with a feedforward neural network, achieve 33 % annual electricity saving without neglecting summer cooling conditions in a hot zone like Miami, being efficient even in energetic-demanding scenarios such as extended working hours.

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

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

Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers

The utilization of energy consumption data is crucial for efficient operation and planning in smart grids. Nonetheless, certain obstacles need to be addressed, such as high computational costs, data security and privacy concerns, and significant expenses associated with installing smart meters across the electrical grid. To address these challenges, generating synthetic data has emerged as a promising approach, providing an opportunity to enhance energy efficiency, demand flexibility, and power grid operation. Therefore, this study proposes a nonlinear model of independent component estimation (NICE) with convolutional layers to produce realistic load profiles. This research aims to evaluate the potential of deep generative models (DGMs) through the characterization and quantification of electricity consumption profiles obtained from an actual smart grid on a university campus. The Kullback–Leibler divergence is used to evaluate the performance of the proposed model. Simulation results show that the proposed model can accurately capture the spatiotemporal correlation of actual samples, leading to synthetic load profiles that closely resemble actual profiles. The performance of the proposed NICE model is compared with a NICE model with dense layers, as well as with Generative Adversarial Networks (GAN) with dense layers, and GAN with convolutional layers (cGAN), all methods previously used in the literature to generate synthetic load profiles. It was observed that the proposed NICE model with convolutional layers leads to better results. This model produces more significant similarity between the probability distributions of actual and synthetic data, in addition to a more extraordinary ability to reproduce more realistic load variability curves.

Identifying hybrid heating systems in the residential sector from smart meter data

In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole.

A novel data balancing approach and a deep fractal network with light gradient boosting approach for theft detection in smart grids

Electricity theft is the largest type of non-technical losses faced by power utilities around the globe. It not only raises revenue losses to the utilities but also leads to lethal fires and electric shocks at distribution side. In the past, field operation groups were sent by the utilities to conduct inspections of suspicions electric equipments stated by the public. Advanced metering infrastructure based recent development in the smart grids makes it easy to detect electricity thefts. However, the conventional supervised learning techniques have low theft detection performance mainly due to imbalance datasets available for training. Therefore, in this paper, we develop a novel theft detection model with twofold contribution. A unique hybrid sampling technique named as hybrid oversampling and undersampling using both classes (HOUBC) is proposed to balance the dataset. HOUBC first performs undersampling and then oversampling using both the majority (normal) and minority (theft) classes. A new deep learning method, fractal network is applied with light gradient boosting method to extract and learn important characteristics from electricity consumption profiles for identifying electricity thieves. The proposed model relies on smart meter's data for theft detection and hence, a rapid and widespread adaption of this model is feasible, which shows its main advantage. The performance of the model is evaluated with real-world smart meter's data, i.e., state grid corporation of China. Comprehensive simulation results describe the effectiveness of the proposed model against conventional schemes in terms of electricity theft detection.