Analysis of Load Profile Generation Methods and Their Effect on the Results of Energy Optimization Models

The primary goal of this paper is to conduct a comparative analysis of various tools used to generate load profile data for residential households and to explore their impact on the results of energy models

Methods and Tools for the Microsimulation and Forecasting of Household Expenditure - A Review

This paper reviews potential methods and tools for the microsimulation and forecasting of household expenditure. It begins with a discussion of a range of approaches to the forecasting of household populations via agent-based modelling tools. Then it evaluates approaches to the modelling of household expenditure. A prototype implementation is described and the paper concludes with an outline of an approach to be pursued in future work

Methods and Tools for the Microsimulation and Forecasting of Household Expenditure

This paper reviews potential methods and tools for the microsimulation and forecasting of household expenditure. It begins with a discussion of a range of approaches to the forecasting of household populations via agent-based modelling tools. Then it evaluates approaches to the modelling of household expenditure. A prototype implementation is described and the paper concludes with an outline of an approach to be pursued in future work

A Social Logic of Energy: A data science approach to understanding and modelling energy transitions of India’s urban poor

Continued use of traditional solid biomass fuels for cooking in Indian households poses a serious public health risk. Particulate emissions in the form of soot contributed to approximately 600,000 deaths in 2019, a burden that falls disproportionately on women, children, and vulnerable populations. Despite over 95% of the population having access to clean cooking fuel distribution, following recent government initiatives to promote liquefied petroleum gas, biomass cooking fuel use is still widespread. This is the case even in cities, where low-income households have low levels of sustained clean cooking fuel use. Interventions to promote transition to clean cooking often focus on cost and technology, informed by an economic-technical view of energy transition, but not all households benefit as expected from these interventions. Previous studies on socio-economic determinants of transition offer limited insight into the reasons for why some households can slip through the net of such interventions. The explanation lies in the socio-cultural and economic heterogeneity across households and the inherent spatial inequalities in urban India. This thesis explores the influence of local socio-economic and cultural factors, and household practices and habits, on clean cooking transition with a view to understanding how the associated heterogeneity can be characterised, and integrated into quantitative energy models and methods. Public national survey and census data is supplemented with primary data collection, which provides valuable quantitative and qualitative data on low-income urban households. Tree-based regression is used to investigate the influence of socio-economic and cultural factors within quantitative models. Determinants are found to exhibit non-linear trends, with thresholds for change in influence on transition. A statistical clustering reveals different typologies of household amongst clean cooking adopters, indicative of different enabling circumstances and pathways to transition. Continued use of biomass is found to be common across recently transitioned households. The heterogeneity amongst low-income households, and the emergent transition pathways, are further investigated through data collected on low-income households in Bangalore. A novel method is used which combines mixed data in a two-stage clustering analysis, offering a means to characterise heterogeneity across households, identifying distinct transition pathways and associated barriers. The findings illustrate how wider socio-economic inequality is intertwined with access to sustained clean cooking. A Bayesian multilevel microsimulation approach is proposed to model the spatial heterogeneity in clean cooking at a city scale. This approach combines publicly available data to generate a synthetic population, and estimates cooking fuel use and fuel stacking using a Bayesian multilevel model. The model takes into account household cooking practices, local spatial effects, and city level economic and policy context. The model reveals how low uptake of clean cooking fuel, and continued biomass use, is related to underlying spatial socio-economic inequalities in cities.Fitzwilliam College, Worshipful Company of Leatherseller

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

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

CGE-Microsimulation Modelling: A Survey

This paper reviews the recent work on the application of the CGE-microsimulation models. The discussion focuses on the various linking methodologies and how they can impact our results.Computable General Equilibrium (CGE) Model; Microsimulation; Poverty; Inequality;

Municipal transitions: The social, energy, and spatial dynamics of sociotechnical change in South Tyrol, Italy

With the aim of proposing recommendations on how to use social and territorial specificities as levers for wider achievement of climate and energy targets at local level, this research analyses territories as sociotechnical systems. Defining the territory as a sociotechnical system allows us to underline the interrelations between space, energy and society. Groups of municipalities in a region can be identified with respect to their potential production of renewable energy by means of well-known data-mining approaches. Similar municipalities linking together can share ideas and promote collaborations, supporting clever social planning in the transition towards a new energy system. The methodology is applied to the South Tyrol case study (Italy). Results show eight different spatially-based sociotechnical systems within the coherent cultural and institutional context of South Tyrol. In particular, this paper observes eight different systems in terms of (1) different renewable energy source preferences in semi-urban and rural contexts; (2) different links with other local planning, management, and policy needs; (3) different socio-demographic specificities of individuals and families; (4) presence of different kinds of stakeholders or of (5) different socio-spatial organizations based on land cover. Each energy system has its own specificities and potentialities, including social and spatial dimensions, that can address a more balanced, inclusive, equal, and accelerated energy transition at the local and translocal scale

Methodological and empirical challenges in modelling residential location choices

The modelling of residential locations is a key element in land use and transport planning. There are significant empirical and methodological challenges inherent in such modelling, however, despite recent advances both in the availability of spatial datasets and in computational and choice modelling techniques. One of the most important of these challenges concerns spatial aggregation. The housing market is characterised by the fact that it offers spatially and functionally heterogeneous products; as a result, if residential alternatives are represented as aggregated spatial units (as in conventional residential location models), the variability of dwelling attributes is lost, which may limit the predictive ability and policy sensitivity of the model. This thesis presents a modelling framework for residential location choice that addresses three key challenges: (i) the development of models at the dwelling-unit level, (ii) the treatment of spatial structure effects in such dwelling-unit level models, and (iii) problems associated with estimation in such modelling frameworks in the absence of disaggregated dwelling unit supply data. The proposed framework is applied to the residential location choice context in London. Another important challenge in the modelling of residential locations is the choice set formation problem. Most models of residential location choices have been developed based on the assumption that households consider all available alternatives when they are making location choices. Due the high search costs associated with the housing market, however, and the limited capacity of households to process information, the validity of this assumption has been an on-going debate among researchers. There have been some attempts in the literature to incorporate the cognitive capacities of households within discrete choice models of residential location: for instance, by modelling households’ choice sets exogenously based on simplifying assumptions regarding their spatial search behaviour (e.g., an anchor-based search strategy) and their characteristics. By undertaking an empirical comparison of alternative models within the context of residential location choice in the Greater London area this thesis investigates the feasibility and practicality of applying deterministic choice set formation approaches to capture the underlying search process of households. The thesis also investigates the uncertainty of choice sets in residential location choice modelling and proposes a simplified probabilistic choice set formation approach to model choice sets and choices simultaneously. The dwelling-level modelling framework proposed in this research is practice-ready and can be used to estimate residential location choice models at the level of dwelling units without requiring independent and disaggregated dwelling supply data. The empirical comparison of alternative exogenous choice set formation approaches provides a guideline for modellers and land use planners to avoid inappropriate choice set formation approaches in practice. Finally, the proposed simplified choice set formation model can be applied to model the behaviour of households in online real estate environments.Open Acces