Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively

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

A probabilistic model to predict household occupancy profiles for home energy management applications

Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles

Development of a multi-energy residential service demand model for evaluation of prosumers’ effects on current and future residential load profiles for heat and electricity

The motivation of this thesis is to develop a multi-energy residential service demand (MESD) model. The approach is based on earlier modelling concepts. Electricity is simu- lated by the help of a first-order Markov-chain approach simulating pseudorandom solar irradiation data as well as occupancy patterns, which are matched to stochastically deter- mined electric appliance activities (McKenna et al., 2015; Richardson & Thomson, 2012). A lumped-parameter model simulating indoor temperatures is utilized to estimate space heating (SH) demand (Nielsen, 2005). Measurement data on domestic hot water (DHW) consumption in dwellings is analysed in order to implement a DHW model. The model generates output in 1-minute resolution. It features various possibilities of dwelling customization: Among others, number of residents, building physics, electric appliances and heating regime may be adjusted. An interface providing a link to the Cambridge Housing Model (DECC, 2012) is implemented, which supports automated re- trieval of relevant building parameters. Electricity and DHW demand values may also be extracted to be used for model calibration. The added value of this work is the implementation of a DHW model and the combination of above named approaches to an integrated multi-energy service demand model. The electricity model is enhanced by improving the calibration mechanism and increasing electric appliance variety. The SH model is extended by random heating regime genera- tion based on field data. The model features full year simulations incorporating seasonal effects on DHW and SH demand. In addition, seven representative archetypes have been developed, which allow for detailed investigation of load profiles for heat and electricity of representative UK dwellings. The model has a wide scope of application. It can be used to explore the impact of differ- ent dwelling configurations on load matching and grid interaction throughout the seasons. Synthetic energy service demand profiles may support research on the optimal configura- tion of on-site supply appliances such as mCHP, PV and heat pumps. Furthermore, the model allows for drawing conclusions on the net carbon emissions of a dwelling and for assessing energy-efficiency measures

Non-parametric modeling in non-intrusive load monitoring

Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM

Stochastic model for electrical loads in Mediterranean residential building: validation and applications

A major issue in modelling the electrical load of residential building is reproducing the variability between dwellings due to the stochastic use of different electrical equipment. In that sense and with the objective to reproduce this variability, a stochastic model to obtain load profiles of household electricity is developed. The model is based on a probabilistic approach and is developed using data from the Mediterranean region of Spain. A detailed validation of the model has been done, analysing and comparing the results with Spanish and European data. The results of the validation show that the model is able to reproduce the most important features of the residential electrical consumption, especially the particularities of the Mediterranean countries. The final part of the paper is focused on the potential applications of the models, and some examples are proposed. The model is useful to simulate a cluster of buildings or individual households. The model allows obtaining synthetic profiles representing the most important characteristics of the mean dwelling, by means of a stochastic approach. The inputs of the proposed model are adapted to energy labelling information of the electric devices. An example case is presented considering a dwelling with high performance equipment.Peer ReviewedPostprint (author's final draft version

A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Unsupervised Learning Based on Markov Chain Modeling of Hot Water Demand Processes

RÉSUMÉ L’ensemble des questions analysées dans ce mémoire dérive d’un important projet de recherche multidisciplinaire appelé smartDESC et réalisé à l’École Polytechnique de Montréal entre les années 2012 et 2016. L’objectif général du projet smartDESC était d’utiliser le stockage associé à certains types de charges d’électricité, et naturellement présent de manière distribuée chez des consommateurs, en vue d’aider à compenser les déséquilibres temporaires entre génération et demande de puissance électrique. Ces derniers sont appelés à devenir de plus en plus fréquents avec la fraction d’énergies renouvelables de type intermittent (énergies solaire et éolienne) dans le mélange de sources d’énergie des réseaux électriques modernes où l’écologie occupe une place de plus en plus importante. Au sein de cet effort général, les chauffe-eau électriques constituent un type de charges d’intérêt particulier vu leur ubiquité et la capacité globale de stockage d’énergie significative à laquelle ils sont associés. Partant d’un ensemble de mesures rendues anonymes de volumes d’extraction d’eau chaude aux 5 minutes, sur une période de plusieurs mois, et fourni par le laboratoire LTE de l’Institut de recherche d’Hydro-Québec, le but de notre recherche était de développer des algorithmes permettant de regrouper des clients individuels en classes de consommation relativement homogènes et dépendantes à la fois du temps de la journée et du jour de la semaine, dans un objectif subséquent de commande coordonnée. Ce faisant, nous devions faire face à trois défis: (i) automatiser la partition des données en segments temporels de durée suffisante pour être statistiquement significatifs, et durant lesquels les statistiques d’extraction d’eau puissent être considérées comme relativement stationnaires; (ii) À l’intérieur de chaque segment temporel, développer des algorithmes d’estimation de paramètres de modèles de chaînes de Markov à deux états (On et Off) d’extraction d’eau avec un paramètre constant par morceaux de taux moyen d’extraction d’eau dans l’état On; (iii) À la lumière des résultats en (ii), développer des algorithmes de classification des usagers en groupes de consommation relativement proches en termes de propriétés statistiques de consommation, selon l’heure de la journée et le jour de la semaine. Dans ce mémoire, des outils de la théorie de l’apprentissage machine, de statistiques, et de la théorie des processus stochastiques sont proposés pour répondre aux trois défis en question.----------ABSTRACT The set of problems tackled in this master thesis is an offshoot of a large multidisciplinary research project called smartDESC or smart Distribution Energy Storage Controller, which was carried out at École Polytechnique de Montréal between 2012 and 2016. The general thrust of the smartDESC project was the coordinated use of storage associated with electric loads at customer sites; the objective of this coordination was to smooth out the uncontrolled generation variability brought about by ecologically friendly, yet intermittent, energy sources such as wind and solar. In that global effort, one particular class of loads of interest because of their ubiquity, and their significant overall energy storage capacity, is that of electric water heaters. We start with a data set consisting of anonymized measurements of hot water extraction volumes in 5 minute samples, over a period of several months, for 73 Quebec households. This data is provided by the LTE laboratory of Institut de recherche d’Hydro-Québec. The goal of the research was to develop approaches to cluster individual users into time of the day and day of the week. We intend to cluster users to relatively homogeneous classes from the point of view of timing and volume of water extraction statistics. Other part of smartDESC is to use these homogeneous clusters to implement coordinated control. In doing so three challenges were to be met: (i) to automate the partition of time of the day into segments of sufficient duration for statistical significance, but relatively stationary hot water extraction statistics; (ii) within each one of the time segments considered, to develop for each user estimation algorithms for two-state (On-Off) Markov chain stochastic models of water extraction with a piecewise constant rate of extraction when On, and validate the results; (iii) In light of the results in (ii), to develop clustering approaches to group users into time of the day and day of the week time intervals where they display relative statistical homogeneity as consumers. In the master thesis, tools from machine learning, statistics and the theory of stochastic processes are used to propose solutions to each of the above three challenges

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC