Residential Sector Energy Consumption at the Spotlight: From Data to Knowledge

Energy consumption is at the core of economic development, but its severe impacts on resources depletion and climate change have justified a call for its general reduction across all economic activities. Lowering households’ energy demand is a key factor to achieve carbon dioxide emission reductions as it has an important energy-saving potential. Households in the European Union (EU28) countries have a significant weight (25%) in the total final energy consumption. However, a wide range of variation is observed within the residential sector from 7.6 to 37.4 GJ per capita/annum, with the lowest consumption indicator observed in Southern EU countries. Energy consumption in the residential sector is a complex issue, explained by a combination of different factors. To pinpoint how to reduce energy consumption effectively while deliver energy services, we need to look not just at technology, but also to the factors that drive how and in what extent people consume energy, including the way they interact with technology (i.e., energy efficiency). The main objective of this research is to understand the differences in energy consumption arising from different socio-demographic, technologic, behavioral and economic characteristics of residential households. This research brings to the spotlight the needs and benefits of looking deeper into residential sector energy consumption in a southern European country. Portugal and the municipality of Évora, in particular, were selected as case studies. Residential sector consumption is a moving target, which increase the complexity of adequate policies and instruments that have to address the bottleneck between increase demand for e.g. climatization due to current lack of thermal comfort and to comply with objectives of increased energy efficiency which ultimately intend to reduce energy consumption. This calls for different levels of knowledge to feed multiscale policies. This dissertation expands the understanding of energy consumption patterns at households, consumers’ role in energy consumption profiles, indoor thermal comfort, and the levels of satisfaction from energy services demand. In a country potentially highly impacted by climate change, with low levels of income and significant lower energy consumption per capita compared to the EU28 average, looking into these issues gains even more importance. The work combines detailed analysis at different spatial (national, city and consumers level) and time scales (hour to annual) taking advantage of diverse methods and datasets including smart meters’ data, door to door surveys and energy simulation and optimization modelling. The results identify (i) ten distinct residential sector consumer groups (e.g., under fuel poverty); (ii) daily and annual consumption patterns (W, U and flat); (iii) major energy consumption determinants such as the physical characteristics of dwellings, particularly the year of construction and floor area; climatization equipment ownership and use, and occupants’ profiles (mainly number and monthly income). It is (iv) recognized that inhabitants try to actively control space heating, but without achieving indoor thermal comfort levels. The results also show (v) that technology can overweight the impact of practices and lifestyle changes for some end-uses as space heating and lighting. Nevertheless, important focus should be given to the evolution in the future of uncertain parameters related with consumer behavior, especially those on climatization, related to thermal comfort and equipment’s use. Furthermore, the research work presents a (vi) bottom-up methodology to project detailed energy end-uses demand, and (vii) an integrated framework for city energy planning. This work sets the ground for the definition of tailor-made policy recommendations for targeted consumer groups (e.g., vulnerable consumers) and climatization behavior/practices to reduce peak demand, social support policies, energy efficiency instruments and measures, renewable energy sources integration, and energy systems planning

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes

Business intelligence in the electrical power industry

Nowadays, the electrical power industry has gained tremendous interest from both entrepreneurs and researchers due to its essential roles in everyday life. However, the current sources for generating electricity are astonishing decreasing, which leads to more challenges for the power industry. Based on the viewpoint of sustainable development, the solution should maintain three layers of economically, ecologically, and society; simultaneously, support business decision-making, increases organizational productivity and operational energy efficiency. In the smart and innovative technology context, business intelligence solution is considered as a potential option in the data-rich environment, which is still witnessed disjointed theoretical progress. Therefore, this study aimed to conduct a systematic literature review and build a body of knowledge related to business intelligence in the electrical power sector. The author also built an integrative framework displaying linkages between antecedents and outcomes of business intelligence in the electrical power industry. Finally, the paper depicted the underexplored areas of the literature and shed light on the research objectives in terms of theoretical and practical implications

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

[EN] An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R-2 values from 0.70 to 0.99 are obtained for daily data resolution and R-2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. (This study has been carried out in the context of RELaTED project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567. This publication reflects only the authors' views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein

Involvement of smart end-users in a Smart Grid

To reach the 20-20-20 goals set by EU in 2009, all parts of the electricity system must be made more efficient. The previous fit-and-forget system must be left behind for a more active grid design. This also means that end-users must become an active part of the power grid. Consumers should be able to actively sell and buy their own energy and control their own usage of energy, or allow for a third party to handle this. A large part of the smart grid will be realized by using computer technology and telecommunication, which can send information to the different parts of the electricity grid. This makes it possible to make complex decisions, based on large quantities of collected data, concerning the most beneficial grid control decisions. This also enables energy efficiency throughout the entire electricity grid, all the way from production through transmission and distribution, including customer premises. This will help Finland reach the 202020 goals, but also achieve a function of the electricity grid that aligns with today’s expectations and demand for functionality. In this thesis the features that may arise from the development of a new smarter electricity grid has been investigated and how these functions align with the ordinary electricity consumers' interest and expectations on functionality. Demand response, distributed generation, energy storage systems, home automation systems and interactive user interfaces are some of the discussed features. The behavior of the end-users was researched through literature studies and by analyzing customer contacts at Fortum. The analysis showed two main reasons for contacting Fortum. Forced contacts, like customers moving, are matters that could be solved to some extent by interactive user-interfaces. The investigative contacts showed customer interest in electricity prices and agreements but also problems with understanding the electricity bill. In this thesis the Rogers' model for diffusion of innovations has also been described and used to analyze smart grid and smart house technology. The main result of the thesis is the definition of a collection of smart house functionalities that would serve as a good base for the development of added value services.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.European Commission, RELaTED: h2020, GA nº 76856

Evidences of lay people’s reasoning related to climate change: per country and cross country results

This deliverable is about lay citizens’ reasoning about sustainability, in particular environmental protection and climate change, in various consumption domains, and the relation of this reasoning to the day-to-day lives of the participants. It presents country and cross-country findings from all 18 STAVE trials conducted between May 2011 and February 2012 in all six PACHELBEL partner countries. Analyses demonstrate that participants in the STAVE trials predominantly display a clear awareness that citizen consumption as demonstrated in their everyday practices of energy use, mobility, waste etc. are strongly connected with issues of environmental sustainablility. The STAVE trials also demonstrated that to live sustainably is a daily challenge, and people are often not able to organize their everyday routines in an environmental-friendly manner. Frequently there is a gap between participants’ aspirations and their practical behaviours. Significantly, the group conversations enabled participants to become aware that the self-assessed soundness of their everyday lives in terms of sustainability was at variance from the actual impact of e.g. their energy use or or mobility practices