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The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems.

The design of reliable and sustainable rural electrification systems relies on accurate prediction of the electrical load. This paper evaluates the current methods for load estimation and proposes an improved approach for load estimation for off-grid unelectrified rural communities that yields more accurate estimates. Improved accuracy is mainly due to better modelling of the influence of customer habits and gender on the estimated current and future load using the Markov chain process. A program was developed using MATLAB software to generate load profiles. The results show that gender considerations have a significant impact on load profiles and that the Markov chain process can suitably be used to determine year-to-year load profiles by incorporating the effect of changes in customer habits on the estimated load. The results from the case study on energy consumption in rural community households showed an increase in average daily consumption when gender was considered during load estimation. The peak consumption when gender was considered was about 50% higher than the value for when gender was not considered., peer-reviewed

En el sector residencial, la paridad de red o grid parity es ya una realidad (o esta cercana) en muchos paises, lo que significa que el coste de la electricidad procedente de las instalaciones fotovoltaicas residenciales es igual o menor que el precio minorista de la electricidad. Sin embargo, no todas las instalaciones son igual de rentables. El objetivo de este trabajo es analizar el rendimiento economico de los sistemas de autoconsumo residenciales, y, en particular, el efecto del perfil de consumo sobre ese rendimiento. Para ello, se propone un modelo de simulacion que permite realizar un analisis comparativo del rendimiento de diferentes configuraciones de un sistema solar residencial conectado a la red. El modelo se aplica a distintos perfiles de consumo diferenciados, asi como diferentes configuraciones de paneles fotovoltaicos y baterias. El analisis permite observar la repercusion de los perfiles de consumo en el autoconsumo, la autarquia, la rentabilidad y, en definitiva, la paridad de red de la instalacion. Palabras clave: Sistemas fotovoltaicos, conexion a red, baterias, simulacion tecno-economica, perfiles de consumo, grid parity

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

This article presents an idea of the implementation of different real load profiles for energy storage system (ESS) operation. The considered approaches are based on real long-term measurements using energy meters, the adaptation of the standard profiles defined by the distribution system operator (DSO), as well as a mix of the level of contracted power and short-term measurements. All combinations are used as electricity demand to formulate an ESS operation plan that cooperates with the PV system and the electricity market. The GAMS solver is applied to obtain optimal operation tasks of the ESS to cover different real and pseudo-real load profiles of an industrial company. Obtained results are presented using a real case study of a metallurgy company with a 317 kWp photovoltaic installation and a 200 kW ESS.

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.

Projections of future energy use in buildings are a crucial tool in the tracking and attainment of political targets for energy efficiency and climate gas mitigation. In this article, a new methodology for projecting both the final energy use and the peak power demand for the Norwegian building stock is presented. The novelty of the methodology is to combine a set of existing, previously documented models in a novel way that integrates building stock models, hourly energy demand load profiles, and energy system modelling. The result is a coherent long-term projection of both annual and hourly energy use for different energy carriers, presented here with four scenarios of final energy use. The results show an expected decrease in total energy use for the Norwegian building stock between −2 and −12 TWh towards 2050, corresponding to a −3% to −14% of the energy use in 2020. Models for projecting future energy use are helpful both to evaluate the potential effects of current policies and to help reveal the need for new or updated policies. However, to have the desired effect, the projections must be as realistic as possible and reflect the actual development in energy use in the building stock. This necessitates a methodology for evaluating historical long-term annual energy use projections to understand why some models succeed in predicting energy use development while others fail. In this article, a set of indicators for evaluating the calibration of different models are presented. The indicators evaluate the initial difference and the divergence in the annualised trend for energy use projection models, compared to statistical data. The indicators are used to compare selected historical energy use projections for the Norwegian building stock against energy use from statistics from 2000 to 2020. The comparison shows large differences between the different projections, where calibrated scenarios show energy savings that tend to be more optimistic in the reference projection but more conservative in the best case potential.

This work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily load patterns of a population with high variability across temporal, geographic, social and economic dimensions.Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more expressive clusters than the zero-one scaler, which is the most common method of normalisation used in the domain. While pre-binning improves clustering results for the dataset, the choice of pre-binning method does not significantly impact the quality of clusters produced. Data representation and especially the inclusion or removal of zero-valued profiles is an important consideration in relation to the pre-binning approach selected. Like several previous studies, the k-means algorithm produces the best results. Introducing a qualitative evaluation framework facilitated the evaluation process and helped identify a top clustering structure that is significantly more useable than those that would have been selected based on quantitative metrics alone. The approach demonstrates how explicitly defined qualitative evaluation measures can aid in selecting a clustering structure that is more likely to have real-world application. To our knowledge this is the first work that uses cluster analysis to generate customer archetypes from representative daily load profiles in a highly variable, developing country context.References:https://doi.org/10.25828/56nh-fw77https://doi.org/10.25828/mf8s-hh79

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.

Demand data is as necessary for energy system analysis as generation data and grid information. When it comes to time series analysis, load profiles are required. In Germany standard load profiles (SLPs) are widely used by network operators and electricity grid modelers. Multiple research and also business offers show that advanced techniques can improve forecasted small consumers loads. Hence, our contribution reviews SLPs and suggested improvements along with other available data. In a case study for a rural region in north-west Germany, assumed load profiles of a grid model are compared to real demand derived measurement data. Additionally, available forecasted and realised power of SLP customers by the distribution system operator are evaluated. The creation of a regional synthetic load profile from this data using a simple approach improved relative root mean square error by approx. 10 % compared to usage of SLPs.

In this study, a novel interface topology called T-type isolated multi-port converter (T-iMPC) is proposed for integrating multi electric vehicles (EVs) with energy-consuming homes in DC microgrid. The proposed converter interface is purposed to use energy units of two different EVs for integration and supervise optimal energy flow between EVs and consumer homes. In the designed system, an FCEV and a BEV are implemented as energy units to manage electrical energy for the vehicle-to-home (V2H) concept. In this context, three energy flow modes are considered for the proposed system: (i) FCEV and BEV are suppliers, (ii) FCEV is supplier, BEV is consumer, and (iii) FCEV is the only supplier. In order to validate the efficacy of the converter operation, a control algorithm is developed to keep the output voltages at a balanced state. The proposed topology is examined under various case studies, including the dynamic load profile of homes for one day and different energy supply modes for FCEV/BEV energy units. Besides, the proposed system is analyzed under various conditions. An assessment and comparison of efficiency involving existing topologies applied in V2H systems are also provided to show the benefits of the proposed converter interface for V2H system in different operating conditions.

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.

Date of Conference: 28 May-1 June 2017 Conference Name: 231st ECS Meeting, 2017 In a recently published article (J. Electrochem. Soc. 164 (2017) A1274-A1280), we described a new method to predict the voltage response of electrochemical energy storage systems during arbitrary load profiles. Our work shows that the impedance spectrum can be employed in the frequency domain in order to ultimately calculate the time domain behavior of the electrochemical energy storage system. The big advantage of this method is the fact that there are no free parameters and fits throughout. The present work deals with the sources of error in the above-mentioned prediction approach and looks for the effects of the various sources of error. The current analysis concludes that two big contributors to the overall error are the inaccuracies in the DC part of the prediction and the non-linearities that are not modeled by a linear impedance spectrum. Discussions are also made regarding ways to improve the performance of the modeling approach the most and where future work is going to be looking to improve.

For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses. ispartof: Energy for Sustainable Development vol:56 pages:98-118 status: Published online

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.

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.