Short Term Electricity Forecasting Using Individual Smart Meter Data

AbstractSmart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is the proposal for accurate short term electricity load forecasting for 24hours ahead, not on the aggregate but on the individual household level

Occupancy Patterns Scoping Review Project

Understanding the occupancy and heating patterns of UK domestic consumers is important for understanding the role of demand-side technologies, such as occupancy-based smart heating controls to manage energy consumption more efficiently.The research undertakes a systematic scoping review to identify and assess the quality of the UK and international evidence on occupancy patterns, to critically review the common methods of measuring occupancy, and to discuss the potential role of occupancy-based smart heating controls in meeting energy savings, thermal comfort and usability requirements.This report was prepared by a team at the University of Southampton and commissioned by the former Department of Energy and Climate Change (DECC).<br/

Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study

Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.36357559

Characterising Domestic Electricity Demand for Customer Load Profile Segmentation

The aim of this research was to characterise domestic electricity patterns of use on a diurnal, intra-daily and seasonal basis as a function of customer characteristics. This was done in order to produce a library of representative electricity demand load profiles that are characteristic of how households consume electricity. In so doing, a household’s electricity demand can be completely characterised based solely on their individual customer characteristics. A number of different approaches were investigated as to their ability to characterise domestic electricity use. A statistical regression approach was evaluated which had the advantage of identifying key dwelling, occupant and appliance characteristics that influence electricity use within the home. An autoregressive Markov chain method was applied which proved to be effective at characterising the magnitude component to electricity use within the home but failed to adequately characterise the temporal properties sufficiently. Further time series techniques were investigated: Fourier transforms, Gaussian processes, Neural networks, Fuzzy logic, and Wavelets, with the former two being evaluated fully. Each method provided disparate results but proved to be complimentary to each other in terms of their ability to characterise different patterns of electricity use. Both approaches were able to sufficiently characterise the temporal characteristics satisfactorily, however, were unable to adequately associate customer characteristics to the load profile shape. Finally clustering based approaches such as: k-means, k-medoid and Self Organising Maps (SOM) were investigated. SOM showed the greatest potential and when combined with statistical and regression techniques proved to be an effective way to completely characterise electricity use within the home and their associated customer characteristics. A library of domestic electricity demand load profiles representing common patterns of electricity use on a diurnal, intra-daily and seasonal basis within the home in Ireland and their associated household characteristics are then finally presented

Electricity consumption pattern disaggregation based on user utilization factor

Non-Intrusive Appliance Load Monitoring (NIALM) technique has been studied intensively by many researchers to estimate the electricity consumption of each appliance in a monitored building. However, the method requires a detailed, secondby- second power consumption data which is commonly not available without the use of high specification energy meter. The common energy meter used in buildings can only capture low frequency data such as kWh for every thirty minutes. This thesis proposes a bottom-up approach for disaggregating kWh consumption of a building. The relationship between the load profile of a building and electricity usage pattern of the occupants were studied and analysed. From the findings, a method based on utilization factor that relates user usage pattern and kWh electricity consumption was proposed to perform load disaggregation. The method was applied on the practical kWh profile data of electricity consumption of Block P19a, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. The disaggregated kWh consumption results for air-conditioning and lighting system were validated with the actual kWh consumption recorded at the respective branch circuits of the building. Results from the analysis showed that the proposed method can be used to disaggregate energy consumption of a commercial building into air-conditioning and lighting systems. The proposed method could be extended to disaggregate the energy consumption for different areas of the building

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Home Occupant Archetypes:

This research is aimed at better understanding how occupants use energy in their homes from a comfort-driven perspective, in order to propose customized environmental characteristics that could improve the occupants’ comfort while reducing energy consumption. To propose such bespoke environmental features and feedback, occupant archetypes were produced based on the intentions and motivations behind comfort behaviours. Building upon the aim of this thesis, the following main research question was proposed: How can energy behaviours be studied from a comfort-driven perspective in order to facilitate the development of environmental features that support more efficient occupant behaviours and that provide the comfort needs of the person? A mixed-methods human-centered design approach was developed for which four steps were required to answer the main research question, reflecting also the four parts of this dissertation. 1. An extensive and multidisciplinary literature review investigated behavioural theories and comfort theories to find out what the drivers behind behaviours are and to understand comfort from a holistic and integrative lens, including social and psychological comfort. Additionally, an overview of energy use in residential buildings was presented, along with the links between energy consumption and occupant behaviours, thus explaining the problems of performance gaps and the rebound effect. The review eventually proposes that energy consumption, behaviours, and comfort are elements of an interacting system, as many behavioural expressions exercised at home are comfort-driven and several of these comfortdriven behaviours result in energy use. This part was the platform on which a questionnaire was developed based on constructs that motivate behaviour: locus of control, attitudes towards energy, environmental needs, and emotions towards home, in addition to other variables such as health status, demographics, and energy consuming habitual actions. Thus, the questionnaire is a tool that consolidates in a single instrument a self-reported assessment of energy consumption patterns and comfort behaviours. The resulting questionnaire was composed of previously validated instruments that were adapted to the context to assess the corresponding constructs and was composed of 65 variables. 2.&nbsp;The newly developed questionnaire was pilot tested with a population consisting of master students of the faculty of Architecture and the Built Environment of the TU Delft. The pilot was launched to make corrections and adjust the questionnaire and to validate the effectiveness of the analysis method to cluster respondents. The TwoStep cluster analysis was chosen as it is a method normally used in the segmentation of health behaviours and was originally developed to group customers in marketing. More recently, it has been used in studies assessing different types of behaviours, especially in the healthcare field. The pilot ensured that the segmentation method was appropriate for the types of variables involved. The cluster analysis produced a model of six clusters, which was successfully validated according to a process that ensures that the groups are both stable and reliable. Subsequently, the questionnaire was administered to the full sample of 761 respondents –mainly composed of students and employees- and was analysed accordingly with the method. The final model was also validated. The final model resulted in five distinct home occupant clusters, which differed on their comfort needs, attitudes towards energy, environmental control beliefs, and emotions towards their home environment. These clusters were the basis of the forthcoming archetypes. 3.&nbsp;In order to better develop the archetypes, occupant-related qualitative data and environment-related quantitative data was needed. A field study was designed to interview occupiers in their homes and to gather building data. To gather building data, a comprehensive checklist inventoried building characteristics related to energy expenditure, such as type of glazing, type of ventilation, type of appliances, etc. Additionally, the indoor environmental parameters (relative humidity, carbon dioxide, and temperature) were monitored, and finally, actual energy consumption readings were taken for a month during the summer period. Parallelly, in-depth and semi-structured interviews were conducted, which are techniques used to gather qualitative behavioural data from the home occupants. Questions related to their energy consuming habits and practices were asked, as well as about their environmental needs for comfort and energy attitudes. Interviews were analysed with a text mining technique: sentiment analysis, which allows assessing the sentiments associated with the topics discussed. Both qualitative and quantitative data were used to complete the previously found statistical clusters, in order to develop the five final archetypes that are the following: Archetype 1: Restrained Conventionals; Archetype 2: Incautious realists; Archetype 3: Positive savers; Archetype 4: Sensitive wasters; Archetype 5: Vulnerable pessimists. 4.&nbsp;Self-reported data and interviews allow collecting explicit knowledge: a type of knowledge that is readily available and is related to facts and memories. When verbally expressed, these facts and memories tend to be processed through&nbsp;biases and conscious filters. As a result, to produce more accurate and complete archetypes, another type of knowledge is also needed: tacit knowledge. This is a type of knowledge is related to feelings, intuitions, and emotions, which tends to be difficult to express with verbalizations. To collect it, focus group sessions were designed to assess the home occupants’ tacit knowledge in terms of what it means to use energy in their homes and what the ideal home experience is. This was collected with the generation of collages that the participants produced with visual and tactile materials, after which they described the process and meanings of their creations. The data was analysed with the use of affinity diagrams that allows to group large amounts of qualitative data into manageable categories and to see the relations between the categories. The results showed two categories: building and occupant, with five sub-categories in total: behavioural aspects, psychological aspects, energy aspects, financial aspects, and home aspects. Each of these subcategories was composed of codes extracted from the collages produced and from the verbal explanations given by the participants. Finally, the data was related back to each of the archetypes, in order to produce final fully-fledged archetypes. The results show that each archetype has different needs, expectations, and experiences as to how they appraise energy and how they desire comfort in their own houses. Consequently, this gives insights into the fact that each of the archetypes is different, they each need differing environmental features to satisfy their comfort needs, to achieve that comfort, and to perceive the impact of their comfort behaviours on the energy outputs of their household. The differing characteristics that each archetype exhibited were translated into preliminary customized design parameters or bespoke environmental features for each of them. They are summed up as follows: the Restrained Conventional needs large windows for a view and a connection to the outside. Because they value personal space and social interaction at home, yet have low environmental control, the plan of the home needs to give a transition from private to social. They are conservative in the energy use and concerned about their finances: energy feedback can be given to them relating their practices to monetary consequences. The Incautious Realist places importance on having the right size and layout for particular purposes: therefore, they need modularity that they can manually control, due to their high external control. They also value safety and privacy, so the interactions with façade elements need to ensure them that their environment is safe and private. They have a high concern about finances, yet they have a high expenditure. To boost their consumption and their need for control, their home can be equipped with a control station from which they can control appliances, and see their consumption as a financial reflection. The Positive Saver places value on the cleanliness and orderliness of the place, thus they need surfaces and spaces that are easy to clean and reach. They are the biggest savers of all the archetypes and this seems to be due to their environmental concerns. To reduce even further their consumption, feedback can be given to them by translating their comfort actions –oven use, etc. - into environmental consequences. The Sensitive Waster needs softness and tactile sensations in their house. They also place importance on having high freedom of their practices in their house. They are the largest energy waster, and they do not worry about their finances, however, they do value the environment and the future. A smart feature can be designed for them to save more energy by equating their practices to ecological consequences to have a more conservative energy use. The Vulnerable Pessimist places emphasis on the aesthetics of the house, the technologies, and the gadgets. They also value a sense of community and connectedness to their neighbourhood. As result, they need homes that allow for these interactions, in small complexes or pavilions. They do not worry about financial aspects, however their expenditure is middle-range: to improve it; they can receive feedback from the consumption of their community as an awareness tool. The findings of this study can help to improve energy predictions, by making more accurate models with different types of occupants. Furthermore, for the existing housing stock, corporations can use the archetypes to tailor the indoor environmental features and interfaces to the future occupant; or, similarly, different occupants can be better allocated to better matching existing dwellings. As for the design of the future stock, architects and contractors can make use of the archetypes by having a more inclusive design process, by answering real needs of the future occupant and improving the decision making of architects. For policies and energy efficiency programs, knowing that there are different types of occupants can allow to bridge gaps between occupant and provider, by encouraging a participatory or inclusive research and design phase, for the design of devices, feedbacks, and interfaces tailored to the specific archetype

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Towards An Enhanced Backpropagation Network for Short-Term Load Demand Forecasting

Artificial neural networks (ANNs) are ideal for the prediction and classification of non-linear relationships however they are also known for computational intensity and long training times especially when large data sets are used. A two-tiered approach combining data mining algorithms is proposed in order to enhance an artificial neural network’s performance when applied to a phenomenon exhibits predictable changes every calendar year such as that of electrical load demand. This approach is simulated using the French zonal load data for 2016 and 2017. The first tier performs clustering into seasons and classification into day-types. The second tier uses artificial neural networks to forecast 24-hour loads. The first tier results are the focus of this. The K-means algorithm is first applied to the morning slope feature of the data set and a comparison is then made between the Naïve Bayes algorithm and the k-Nearest Neighbors algorithm to determine the better classifier for this particular data set. The first tier results show that calendar-based clustering does not accurately reflect electrical load behavior. The results also show that k-Nearest Neighbors is the better classifier for this particular data set. It is expected that by optimizing the data set and reducing training time, the learning performance of ANN-based short-term load demand forecasting