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In this paper, a methodology for estimating end-use load shapes using the hourly whole-house metered load data, the household demographic survey data and the weather data (temperature) is presented. End use load shapes presents a method of generating realistic electricity load profile data for some of city of Tripoli domestic buildings. This method could help in predicting the daily load profile from individual flats to community. The results obtained show that the overall methodology provides an effective means for end-use load shape modeling and estimation.

Predicting the electricity demand of new supermarkets will help with design, planning, and future energy management. Instead of creating complex site-specific thermal engineering models, simplified statistical energy prediction models as we propose can be useful to energy managers. We have designed and implemented a data-driven method to predict the ‘electricity daily load profile’ (EDLP) for new stores. Our preliminary work exploits a data-set of hourly electricity meter readings for 196 UK supermarkets from 2012 to 2015. Our method combines the most similar stores on a feature space (floor area split by usage such as general merchandise, food retail and offices and geographical location) to obtain a prediction of the EDLP of a new store. Computational experiments were performed separately for subsets of supermarkets that consume only electricity, both electricity and gas, and by season. The best results were obtained when predicting Summer EDLPs with stores using electricity only. In this case, the average Manhattan difference and the percentage difference are 234 kWh and 16%, respectively. We aim to develop an application tool for supermarket energy managers to automatically generate EDLP for potential new stores. EPSRC Impact Acceleration Account - University of Oxford EP/R511742/1

Graphical abstract, The spread of the COVID-19 pandemic caused a tremendous impact on our societies, including changes in household energy consumption. Using measured electricity use data from 500 homes in Ottawa, Canada, this study applies changepoint analysis, descriptive statistics, k-means clustering, and the corresponding change of electricity utility bills before and after COVID-19. Our analysis indicates that the average household daily electricity consumption increased by about 12% in 2020 relative to 2019, about one-third was due to warmer temperatures, with much of the rest due to the temperature-independent loads (e.g., lighting and appliances). Additionally, the highest five peak loads corresponding to post-COVID are significantly higher (15–20%) than peaks that occurred pre-COVID. The lockdown’s impact on household electricity use is not consistent, and there are noticeable differences among different months, seasons, and day types. Two clusters of household electricity use patterns emerged, with about one-third showing significant increases during the pandemic and the remainder showing only minor changes. On the other hand, in the summer, all customers’ electricity use profile patterns after the pandemic resemble the pattern before the pandemic. Yet, there is a significant increase (from 16.3 to 29.1%) in daily demand after COVID-19. Finally, the average increase in the utility bill post-COVID would be 9.71% if TOU rates were used instead of the flat rate that was implemented as a subsidy to consumers.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.

Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications.

Buildings are a key source of energy flexibility due to their high energy demand. Harnessing the energy flexibility of buildings, however, demands that buildings be considered collectively. This paper presents preliminary results to discover building's energy flexibility, from two different neighborhoods in the Netherlands. The energy demand profiles of three large buildings in each neighborhood are analyzed to identify possible useful simultaneous heating and cooling loads. Finally, the possibility for energy exchange between these buildings is explored.

Rural electrification plays a key role in terms of achieving the target of universal access to electricity by 2030. In order to deploy reliable and sustainable solutions, the sizing phase of decentralized power systems requires specific inputs such as long-term forecasts of the electricity load. In rural contexts, access to electricity is extremely interlinked to social-economic complex dimensions that in turn, influence the electricity demand. In this paper, we rely on System Dynamics for modelling such complexities, and we calibrate the results with data from a hydroelectric-based electrification programme implemented in 2005 in the rural community of Ikondo, Tanzania. We coupled the model with a newly extended version of a load stochastic tool, namely Lifetime – LoadProGen, in order to introduce a more reliable and calibrated approach to long-term electricity load forecasts.

The increase in the number of electric vehicles leads to an increased demand for residential charging. While EV electric loads can have a negative impact on the power grid, they also represent a large potential for energy flexibility. This study proposes a methodology to describe charging habits, electricity load profiles, and flexibility potentials of EV charging in apartment buildings. The input data used for the method are generally available for buildings with multiple EV charge points: EV charging reports with individual charging sessions and aggregated smart meter data. The case study is a large housing cooperative in Norway, with a combination of private and shared charge points for the residents. The study compares two charging power assumptions of 3.6 kW and 7.2 kW. The flexibility potential increases with higher charging power. The study reveals a significant potential for residential EV charging flexibility when private parking spaces have EV charge points.

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

At the moment of writing, in Italy, there is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its outbreak is leading to severe global socioeconomic disruptions impacting on all economic sectors from tourism, industry and the tertiary sector, up to the operational and opening of public offices, the closure of schools and the organization of families. Measures adopted by the Italian government to deal with the COVID-19 emergency have had direct effects both on people’s daily lives and on the activity of most industrial and commercial production companies. These changes have been unequivocally reflected also on the Italian electricity system, which has shown unprecedented behavior in terms of both energy consumption and volume—and subsequently, in the observed share of renewable and conventional production technologies. The goal of this study is to show the impact on the power industry of all the restrictions and lockdown of the activities in Italy and to discuss the effects of COVID-19 outbreak on the bulk power system and the entire electricity sector. In particular, the consequences on load profiles, electricity consumption and market prices in Italy, including the environmental aspects, are examined.

In recent years, the new achievements in the field of technology and data science allowed to gather detailed and well-structured information about electricity consumption behaviors of industrial enterprises. Such type of information can find numerous applications in the power distribution industry. The utilities often use the data from contracts to assign each industrial customer a class label according to this type defined in predetermined industry segmentation. Such type of fixed-chart segmentation is not able to satisfy the needs of modern enterprises for the flexible and dynamic determination of production modes. In this paper, we address this problem by proposing a new method for the segmentation of various types of factories based on their electricity consumption patterns represented in load profile data. It exploits the evolution-based characteristics of smart meter data of multiple types of factories to remove irrelevant features. We use data visualization to estimate the number of clusters and apply the well-known $k$ -means algorithm on filtered data to generate segmentation. Experimental results on real load profile data collected with smart meters from manufacturing industries in Guangdong province of China have shown that the new clustering approach produced the meaningful segmentation of factories that reflect production operations.

and Conclusion The contribution points out modelling results from a techno-economic analysis of battery supported PV systems. Especially the ratio of the installed PV peak power to the useable capacity of the battery has a big impact on the rate of self-consumption, autarky and economic efficiency. Further on the impact of different household load profiles is analysed with focus on cost optimised system designs. The calculations are model-based and take into consideration the consumer load profile, technical and economical PV and battery system parameters as well as the framework of regulation. Results include the cost optimal system configuration, the operator's share of self-consumption and degree of autarky, grid feed-in and supply and various battery system parameters. One key finding is the importance to use realistic load profiles because otherwise the modelling results can be too optimistic for self-consumption and total costs. That is especially the case for aggregated profiles that do not represent a real consumer.

This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffic, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.

Demand-side management methods are expected to play a key role in future energy systems. These methods are making energy demand adaptive to fluctuations inherent to intermittent renewable energy sources. In order to develop feasible demand-side management solutions, a better understanding of the temporality of the energy demand is needed. This paper contributes to this by focusing on the daily load profiles of energy demand for heating of Danish dwellings with heat pumps. Based on hourly recordings from 139 dwellings and employing cluster and regression analysis, the paper explores patterns (typologies) in daily heating load profiles and how these relate to socio-economic and technical characteristics of the included households. The study shows that the load profiles vary according to the external load conditions. Two main clusters were identified for both weekdays and weekends and across load segments; a main cluster with a relatively constant load profile and a minor cluster with a more distinct variation during the day. The difference between the clusters is primarily correlated with building characteristics like floor area, building year and type of space heating distribution system, while the existence of children and teenagers in the household were the only significant socio-economic variables.

This paper presents a sequential Monte Carlo (MC) method for the evaluation of the distribution network reliability, by considering the formation of microgrids supplying different portions of the network. The MC technique considers interfering near-coincident faults, as well as a mix of load profiles (residential, industrial and commercial) and renewable generation patterns (wind, photovoltaic, geothermal and waste-to-energy). Unavailability of distributed generators as well as mobile generation systems is checked in the model. In addition to this, the model takes into account two initial configurations (covering peak and off-peak hours, respectively), for getting information about the variation of the reliability when the network is operated with an intraday reconfiguration strategy. In this framework, the main challenge is the improvement of the continuity of supply level in distribution systems, allowing autonomous and intentional islanded subsystems (e.g., microgrids). The effectiveness of the proposed method is shown in a real network.

Zero Energy Buildings (ZEBs) are considered as one of the key elements to meet the Energy Strategy of the European Union. This paper investigates cost-optimal solutions for the energy system design in a ZEB and the subsequent grid impact. We use a Mixed Integer Linear (MILP) optimisation model that simultaneously optimises the building’s energy system design and the hourly operation. As a ZEB have onsite energy generation to compensate for the energy consumption, it is both importing and exporting electricity. The hourly time resolution identifies the factors that influence this import/export situation, also known as the building’s grid impact. An extensive case study of a multi-family house in Germany is performed. The findings show that the energy system design and the grid impact greatly depend on the ZEB definition, the existing policy instruments and on the current energy market conditions. The results indicate that due to the feed-in-tariff for PV, the cost-optimal energy design is fossil fuelled CHP combined with a large PV capacity, which causes large grid impacts. Further, we find that heat pumps are not a cost-optimal choice, even with lower electricity prices or with increased renewables in the electric power system. Acknowledgements. This work has been supported by the Research Council of Norway and several partners through the Research Centre on Zero Emission Buildings (ZEB) and the Research Centre for Sustainable Energy Studies (CenSES). The authors acknowledge the support from the Norwegian Water Resources and Energy Directorate (NVE) in Oslo, Norway, and Fraunhofer Institute for Solar Energy System (ISE) in Freiburg, Germany. The first and second authors would like to thank Viessmann for providing technology cost data for current market conditions in Germany. The first author would also like to thank Dr. Thomas Erge with the Fraunhofer ISE for valuable input on the current energy market conditions in Germany, and Dr. Torgeir Ericson with the NVE for constructive comments on the structure of the paper.

According to EU’s Energy Performance of Buildings Directive (EPBD), all new buildings shall be nearly Zero Energy Buildings (ZEB) from 2018/2020. How the ZEB requirement is defined has large implications for the choice of energy technology when considering both cost and environmental issues. This paper presents a methodology for determining ZEB buildings’ cost optimal energy system design seen from the building owner’s perspective. The added value of this work is the inclusion of peak load tariffs and feed-in-tariffs, the facilitation of load shifting by use of a thermal storage, along with the integrated optimisation of the investment and operation of the energy technologies. The model allows for detailed understanding of the hourly operation of the building, and how the ZEB interacts with the electricity grid through the characteristics of its net electric load profile. The modelling framework can be adapted to fit individual countries' ZEB definitions. The findings are important for policy makers as they identify how subsidies and EPBD’s regulations influence the preferred energy technology choice, which subsequently determines its grid interaction. A case study of a Norwegian school building shows that the heat technology is altered from HP to bio boiler when the ZEB requirement is applied. Acknowledgments. This work has been supported by the Research Council of Norway and several partners through the Research Centre on Zero Emission Buildings (ZEB) and the Research Centre for Sustainable Energy Studies (CenSES). The authors also acknowledge the support from the Norwegian Water Resources and Energy Directorate (NVE) in Oslo, Norway, and Fraunhofer Institute for Solar Energy System (ISE) in Freiburg, Germany.

[EN] The following article describes the work of dynamic segmentation of daily load profiles throughout years 2008 and 2009, of a representative sample of Spanish residential customers. The technique applied is classification of the energy consumption time series of load profiles by means of dynamic clustering algorithms. The techniques used and analysis performed prove adequate as a fast tool to classify clients according to their energy consumption patterns, as well as to evaluate their overall energy consumption trends at a glance. The segmentation of the energy consumption load profiles is performed, and the results are analyzed and discussed., The works developed in this document have been possible thanks to Iberdrola Distribucion Electrica S.A.U. and the development of the GAD project. The GAD or "Active Demand Management" (in Spanish) project was a project supported by the Spanish Government, and participated by 14 different companies and 14 research centers. It was sponsored by the CDTI (Technological Development Centre of the Ministry of Science and Innovation of Spain), and financed by the INGENIO 2010 program.

This paper presents results from a field trial with a hybrid micro-cogeneration system applied to satisfy both thermal and power needs of a typical Canadian detached house. The hybrid system consists of an internal combustion engine integrated with a high efficiency furnace. It was installed, integrated with the rest of the mechanical systems and demonstrated at the Canadian Center for Housing Technology (CCHT). Cost analysis was performed to examine the economics of such systems in relation to different price structures imposed by the electric utilities. The study revealed that the hybrid micro-cogeneration system performed reliably and generated both heat and power with high efficiency during the heating season and was able to satisfy the heating demand under very cold weather conditions. The power generated by the system was first directed to the house satisfying 50% of the electric load with the other 50% being exported to the grid. The project demonstrated that micro-cogeneration systems when sized to meet the base electric load and design thermal load of the residence are a valuable alternative to the central power generation plants and are able to reliably satisfy the house thermal/power demand even under extreme cold conditions.

International audience; To face winter electricity peaking issues the authors proposes an analysis of the potential of distributed gas technologies for demand side management. This impact has to be analysed at small scale before any large scale extrapolation. Bi-energy technologies (gas and electricity) are a path to transfer loads from one system to another. Indeed, the flexible gas infrastructure adapts to load while electricity demand variations cause risk of black-out. The impacts of two hybrid technologies are studied at transformer level with 1-min experimental load profiles of 40 dwellings equipped with micro Combined Heat and Power (μCHP) boilers over a year in France. An absolute peak load reduction by 17% at small scale is found. Different technology mixes are then simulated to assess the effect on local infrastructure. Finally a methodology for temperature dependence analysis of load is used to assess different potential benefits of gas technologies.

The paper presents a microcontroller-based automatic scheduling system for the operation and control of the distributed energy resources connected to residential electric power installations, taking into account both the following operating conditions: 1) the grid connected operating mode, in which the household electric circuits are connected to the distribution network; and 2) the islanded operating mode, in which the household electric system is able to feed at least part of the load although being disconnected from the public distribution network. The considered residential electric power is assumed to include various distributed energy resources, also of innovative type, such as fuel cells and photovoltaic systems, and an energy storage device, such as a battery unit connected to the system through a voltage-source bi-directional power electronic converter, which allows the load balancing by means the battery charge and discharge in every operating condition. The operating behavior of the proposed automatic system is verified by using an experimental set-up equipped with commercial PEM fuel cell, a lead-acid battery storage system, a PV array emulator, and variable active and reactive loads of few kilowatts which are able to reproduce different load profiles. © 2009 IEEE.

This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.

This work uses a probabilistic method to combine two unique datasets of real world electric vehicle charging profiles and residential smart meter load demand. The data was used to study the impact of the uptake of Electric Vehicles (EVs) on electricity distribution networks. Two real networks representing an urban and rural area, and a generic network representative of a heavily loaded UK distribution network were used. The findings show that distribution networks are not a homogeneous group with a variation of capabilities to accommodate EVs and there is a greater capability than previous studies have suggested. Consideration of the spatial and temporal diversity of EV charging demand has been demonstrated to reduce the estimated impacts on the distribution networks. It is suggested that distribution network operators could collaborate with new market players, such as charging infrastructure operators, to support the roll out of an extensive charging infrastructure in a way that makes the network more robust; create more opportunities for demand side management; and reduce planning uncertainties associated with the stochastic nature of EV charging demand.

The COVID-19 pandemic and the associated lockdown can be regarded as a forced social experiment, the results of which show how to use energy under specific conditions. During this period, there was a reduction in electricity consumption at the level of the power system, but a different specificity distinguishes the group of household users. The article aims at presenting and analysing the identified issues concerning residential electricity users based on the experience from the COVID-19 pandemic lockdown. Data from energy meters from almost 7000 flats in Warsaw’s housing estates during the lockdown in 2020 and the analogous period before the pandemic were used. The analysis showed that, on average, residential users staying practically the whole day in their flats increased their energy consumption, but without increasing their average daily peak power, smoothing the profile in the morning hours to the level reaching the peak power that had occurred in the analogous period before the lockdown. The peak power of the sections feeding the different numbers of dwellings also remained practically unchanged during the lockdown compared to the pre-pandemic period. The pressure to work and educate remotely should contribute to an increase in the digital competence of society, which may result in an increased interest in new forms of activity and cooperation based on demand-side response and prosumption mechanisms, with digital settlements for energy exchange and services.