Variability of behaviour in electricity load profile clustering: who does things at the same time each day?

UK electricity market changes provide opportunities to alter households' electricity usage patterns for the benet of the overall electricity network. Work on clustering similar households has concentrated on daily load proles and the variability in regular household behaviours has not been considered. Those households with most variability in reg- ular activities may be the most receptive to incentives to change timing. Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load prole clustering. 204 UK households are analysed to nd repeating patterns (motifs). Variability in the time of the motif is used as the basis for clustering households. Dierent clustering algorithms are assessed by the consistency of the results. Findings show that variability of behaviour, using motifs, provides more consistent groupings of households across dierent clustering algorithms and allows for more ecient targeting of behaviour change interventions

Characterization of electricity demand based on energy consumption data from Colombia

The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic

Role of household activities in peak electricity demand and distributional effects of Time-of-Use tariffs

Introduction of Time-of-Use (ToU) tariffs have the potential to motivate consumers to flex their energy use and, by utilising their flexibility, support the reduction in peak electricity demand. In return, lower peak demand could also reduce the system costs due to the reduced need for peaking generation and network reinforcement. By their nature, ToU tariffs would penalise consumers with high consumption during peak periods and who are not able to exercise flexibility. Therefore to ensure the affordability of energy bills it is important to understand the relationship between the timing of activities in the household and socio-demographic properties of the consumers. This paper uses UK Time Use survey data to cluster households by their energy-related activities during the peak electricity demand periods, model the corresponding electricity demand and analyse the impact of ToU tariffs across several socio-demographic parameters. Results show that similar patterns of energy related activities exist for the clusters with different socio-demographic parameters (e.g. family structure or income). Findings also show that there is no single dominant socio-demographic parameter that defines the winners or losers from the introduction of ToU tariff

Comparison of clustering techniques for residential load profiles in South Africa

This work compares techniques for clustering metered residential energy consumption data to construct representative daily load profiles in South Africa. The input data captures a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine their effect on producing a good clustering structure. A Combined Index is developed as a relative score to ease the comparison of experiments across different metrics. The study shows that normalisation, specifically unit norm and the zero-one scaler, produce the best clusters. Pre-binning appears to improve clustering structures as a whole, but its effect on individual experiments remains unclear. Like several previous studies, the k-means algorithm produces the best results. To our knowledge this is the first work that rigorously compares state of the art cluster analysis techniques in the residential energy domain in a developing country context

Evaluation of clustering techniques for generating household energy consumption patterns in a developing country

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 contex

A data-centric stochastic model for simulation of occupant-related energy demand in buildings

If greenhouse emission reduction targets are to be met worldwide, not only will there need to be major investment in decarbonisation of the electricity supply network, but there will also need to be a significant reduction in energy demand. The built environment offers opportunities for demand reduction that can help to achieve the necessary targets. For buildings yet to be built there is a real opportunity to design low-energy environments, but the existing building stock will largely still be in existence in 50 years and so retrofit options must also be explored. No matter how efficient a building it is the occupants that drive the energy consumption - whether by requiring comfortable conditions or by using electrical equipment. While in the residential sector owner-occupiers have particular responsibility for consumption, in the non-domestic sector the financial responsibility may not lie with the building occupants and hence it is harder to target demand reduction interventions. In addition, while in a residential building the behaviour of the individual has a direct and significant impact on energy consumption, in a non-domestic building it is the collective behaviour that is important to understand. The impact of the building occupants on internal loads is critical to assessing the energy efficiency of a design or retrofit. Building energy simulation offers a means to assess the potential benefits of different options without requiring costly in-situ tests. In order for the approach to be viable, however, a simulation needs to demonstrably replicate the building performance. This has proved to be difficult not only pre-construction but even for operational demand, in part because individual and collective occupant behaviour is difficult to quantify. Typically, building energy simulation packages require occupant-related internal loads to be input into the simulation via a deterministic schedule consisting of a peak daily demand and a diversity schedule that describes how the demand varies over a 24hr period. The stochastic nature of occupant-related energy demand is well known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. A new approach is required. The aim of this thesis is to develop a new model for the definition of occupant-related building internal loads for input into building energy simulation. Early studies showed that a model must not only be able to generate good estimates of the key parameters of interest with a measure of the uncertainty, but must also be able to assimilate data, be able to simulate operational change and be straightforward to use. All buildings generate monitored data of some form, even if it is just monitored consumption for purposes of billing. Since the start of the century there has been a rapidly increasing pool of monitored data at increasing time and spatial resolutions for both residential and non-domestic buildings. Increasing monitoring of electricity consumption generates an opportunity to gain an in-depth understanding of the nature of occupant-related internal loads. The requirement for a model to be able to assimilate these data make a data-centric model a natural choice. This study focuses on non-domestic buildings and the collective stochastic behaviour of the occupants as evidenced by monitored plug loads and lighting demand. Using monitored data from four sub-metered buildings across the Cambridge University building stock a functional data analysis approach has been used to extract the underlying structure of the data in a way which facilitates generation of new data samples that encompass the observed behaviour without replication. A key assumption in simulation of non-domestic buildings is that the internal loads are in some way related to the activity that takes place in a building zone. This is problematic both because the definition of activity is indeterminate and because building sub-metering strategies rarely align with the specified activities. Deconstruction of the data allows exploration of this fundamental assumption and leads to the conclusion that activity per se is not a good indicator of internal loads. Instead, for plug loads it is the expected variability of the data that is important, whereas for lighting the control strategy of each individual building zone defines the stochasticity of the demand. The model has been developed into a practical online tool for generation of plug loads and lighting demand in the form of annual hourly time histories of internal load that can be input directly into a building energy simulation. As a design tool the modeller can select an expected level of variability in demand and use estimated base load and load range to generate synthetic demand profiles. The beauty of the approach is that if monitored data are available - for example when optimising retrofit designs - the data can be used to generate synthetic time histories that encompass observed demand but can also be modified to account for operational change - a reduction in minimum daily demand for example. Finally this thesis suggests a potential alternative to the activity-based deterministic approach for the specification of occupant-related building internal loads. Rather than generating loads for each new simulation on a case by case basis, the suggestion is to use an approach similar to that used for the specification of weather data - another stochastic input. The proposal is to create annual hourly stochastic samples of typical demand according to the expected variability. These would be used with user-defined energy use intensity values with scenarios for extreme demand in much the same way that typical and future weather scenarios are modelled. The methodology presented here is one such way to generate annual hourly stochastic sample data and provides an initial step towards the specification of such typical load profiles.Laing O'Rourk

Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments" added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community"s demand volume and the consumer"s flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.Comunidad de Madri

Residential Demand Response using Electricity Smart Meter Data

The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited. The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem". This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC