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The challenge of quantifying sustainability is continuously debated in the building industry. There is an increased need for quantitative solutions to be developed for measuring and comparing performance. The Danish government has proposed a voluntary sustainability program to lift the bottom of the industry with regard to sustainability performance. This study evaluates the possibilities of increasing the sustainability reference level in the building industry based on expert interviews. The study was framed upon the multi-level perspective as a theory to describe the transition to sustainability. The results indicated that existing knowledge and experience within the topics of the regulatory changes is high in the industry. Therefore, the impact of the changes would not be very effective if they are not sufficiently innovative or demanding. Regarding the effect of the voluntary sustainability program, it was found that the program is not very ambitious and likely to only have an impact on companies with a lower level of ambition in terms of sustainable initiatives. The hope, however, is that it will keep raising the level of sustainability.

Air pollution is a global challenge, and especially urban areas are particularly affected by acute episodes. Traditional approaches used to mitigate air pollution primarily consider the technical aspects of the problem but not the role of citizen behaviour and day-to-day practices. ClairCity, a Horizon 2020 funded project, created an impact assessment framework considering the role of citizen behaviour to create future scenarios, aiming to improve urban environments and the wellbeing and health of its inhabitants. This framework was applied to six pilot cases: Bristol, Amsterdam, Ljubljana, Sosnowiec, Aveiro Region and Liguria Region, considering three-time horizons: 2025, 2035 and 2050. The scenarios approach includes the Business As Usual (BAU) scenario and a Final Unified Policy Scenarios (FUPS) established by citizens, decision-makers, local planners and stakeholders based on data collected through a citizen and stakeholder co-creation process. Therefore, this paper aims to present the ClairCity outcomes, analysing the quantified impacts of selected measures in terms of emissions, air quality, population exposure, and health. Each case study has established a particular set of measures with different levels of ambition, therefore different levels of success were achieved towards the control and mitigation of their specific air pollution problems. The transport sector was the most addressed by the measures showing substantial improvements for NO2, already with the BAU scenarios, and overall, even better results when applying the citizen-led FUPS scenarios. In some cases, due to a lack of ambition for the residential and commercial sector, the results were not sufficient to fulfil the WHO guidelines. Overall, it was found in all cities that the co-created scenarios would lead to environmental improvements in terms of air quality and citizens’ health compared to the baseline year of 2015. However, in some cases, the health impacts were lower than air quality due to the implementation of the measures not affecting the most densely populated areas. Benefits from the FUPS comparing to the BAU scenario were found to be highest in Amsterdam and Bristol, with further NO2 and PM10 emission reductions around 10%–16% by 2025 and 19%–28% by 2050, compared to BAU.

Decarbonizing the district heating sector is essential for an affordable energy transition. Decarbonization strategies focusing on the interaction between power sector, individual heating technologies, heat-saving measures, and district heating expansion are missing in literature. We use energy system investment optimization to analyse the impact of decarbonization policies on district heating by comprehensively modelling the above-mentioned interaction across power and heating sector, and within the heating sector. We model decarbonization policy scenarios of massive investment into heat-saving measures and an earlier natural gas phase-out by 2030. Using Denmark as a case study, we identify policy-driven investments into heat-saving measures as non-optimal. Our results indicate that district heating expansion facilitates an earlier natural gas phase-out, which is a viable policy that inflicts only 0.07% extra system cost compared to a scenario with only net-zero emission targets. Furthermore, we find that decarbonized district heating sector has a very high reliance on industrial excess heat, biofuels, and electrification. A biomass tax reduces the high utilization of raw biomass and promotes its more sustainable use in carbon capture and storage. Our results show a cogeneration capacity reduction, which poses a challenge to the business model of conventional Danish district heating companies. Publicationsupported byEMB3Rsproject

District heating (DH) is often regarded as a sustainable supply of heat - improving energy efficiency and contributing to net zero emission targets. We analyse the expansion potential of DH in several EU Countries from the perspective of the whole energy system, which comprises interconnected heat power and transport sectors under different scenarios of heat demand development. Our results show a strong potential for DH expansion in most of the countries analysed with an overall share of DH increased from 8% to 40%. Among the countries analysed, Germany, Netherlands, UK, France, and Norway showed the most potential as presently, the DH in these Countries is almost non-existent. The role of potential exploitation of waste heat from electrolysers is also evaluated, and the results show a promising expansion with more than 50 TWh of excess heat introduced in the district heating grid with a 4% higher expansion of DH. We ultimately highlight the need for a comprehensive policy and market framework to promote expansion of energy efficient DH system and achieve an affordable decarbonization of the heating sector. Publication supported by EMB3Rs project

The production of waste heat - or excess heat is substantial in the European Union. Despite the potential for excess heat utilisation several barriers exist. This paper identifies different barriers and enablers for excess heat utilisation, which is based on an analysis carried out in four European countries as part of EU funded project. The detailed literature review on the status of excess heat recovery identified a very diverse nature of district heating regulations in the different countries along with different layouts of contracts and business models. An analysis of the potential for excess heat utilisation in Denmark shows a substantial increase in excess heat utilisation in district heating. The identified barriers include access to a market (district heating network), lack of a comprehensive regulatory framework along with standardising contracts, lacking sinks (end users), and absent financial incentives to make excess heat a viable option for the potentially involved stakeholders.&nbsp

Contrary to electricity smart meter data analysis, little research regarding district heat smart meter data has been published. Previous papers on smart meter data analytics have not investigated autocorrelation in smart meter data. This paper examines district heat smart meter data from the largest district heat supplier in Denmark and autocorrelation is identified in the data. The K-Means algorithm is not able to take autocorrelation into account when clustering. We propose different data transformation methods to enable K-Means to account for this autocorrelation information in the data by using wavelet transformation and autocorrelation features. Our results show that the K-Means yield acceptable clustering results for district heat data when clustering normalized data, inclusion of autocorrelation improves the clustering. The clusters on normalized data are similar to the wavelet transformed clusters, where the autocorrelation has been accounted for. The clustering achieved with the autocorrelation transformation yields finer clusters through accounting for autocorrelation. We are not able to statistically show a difference between the transformations. All transformations result in shadowing clusters, but the autocorrelation transformation generates fewer shadow clusters and reduce the number of dimensions from 744 to 24, resulting in a dramatic reduction in K-Means runtime.

The development of city-driven urban laboratories was considered a priority by the European Commission through Action 3.2 of the Strategic Energy Technology Plan. In this context, positive-energy districts laboratories could take the role of urban drivers toward innovation and sustainability in cities. These urban labs can provide real-life facilities with innovative co-creation processes and, at the same time, provide testing, experimenting, and prototyping of innovative technologies. In this scope, the authors of this work want to share the very first results of an empirical study using the testing facilities provided by the members of the Joint Program on Smart Cities of the European Energy Research Alliance as positive-energy districts laboratories. Six climatic regions are studied as boundary conditions, covering temperate and continental climates. Four scales of action are analyzed: Building, campus, urban, and virtual, with building and campus scales being the most frequent. Most of these laboratories focus on energy applications followed by networks, storage systems, and energy loads characterization. Many of these laboratories are regulated by ICT technologies but few of them consider social aspects, lighting, waste, and water systems. A SWOT analysis is performed to highlight the critical points of the testing facilities in order to replicate optimized configurations under other conditions. This statistical study provides guidelines on integration, localization, functionality, and technology modularity aspects. The use of these guidelines will ensure optimal replications, as well as identify possibilities and opportunities to share testing facilities of/between the positive-energy district laboratories.

Monitoring abnormal energy consumption is helpful for demand-side management. This paper proposes a framework for contextual anomaly detection (CAD) for residential energy consumption. This framework uses a sliding window approach and prediction-based detection method, along with the use of a concept drift method to identify the unusual energy consumption in different contextual environments. The anomalies are determined by a statistical method with a given threshold value. The paper evaluates the framework comprehensively using a real-world data set, compares with other methods and demonstrates the effectiveness and superiority.

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences.

Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

Different data sources, data types and platforms are involved in modelling emissions load profiles. In our case, we model emission load profiles at the regional or city level. However, we found missing values, redundancy and inconsistency in the datasets, and in most cases data preprocessing is unavoidable. Data pre-processing converts the data into a clean and tidy dataset for the subsequent modelling steps or statistical analyses. Therefore, some common techniques for data pre-processing such as cleaning, transformation, integration, reduction and some terms in data mining such as filtering and selection have been applied in our case study. We usually do the data pre-processing of moderate problems and a small amount of data using a spreadsheet application, whereas we use the programming language to do the more complex and big data size tasks. As a result, it has been found that understanding the nature of our data collection, the data flow process and the desired output comprehensively is the key for efficiency in data pre-processing. The applied techniques have helped us to provide the proper input for modelling the regional emission load profile efficiently.

Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and consumption intensity. It also supports the segmentation analysis according to the social characteristics of customers of individualhouseholds or neighborhoods, as well as spatial geometries. SEGSys uses a three-layer architecture to model the segmentation system, including the data layer, the service layer, and the presentation layer. The data layer models data into a star schema within a data warehouse, the service layer provides data service through a RESTful interface, and the presentation layer interacts with users through a visual map. This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system.

Real-time energy consumption monitoring is becoming increasingly important in smart energy management as it provides the opportunity for novel applications through data analytics, including anomaly detection, energy leakage, and theft. This paper presents a smart non-intrusive load monitoring approach for residential households, collecting fine-grained energy consumption data and disaggregating the data of appliances. The paper describes the implementation of the monitoring system, the data set, load disaggregation, and the challenges for future work.

Data warehousing populates data from different source systems into a central data warehouse (DW) through extraction, transformation, and loading (ETL). Massive transaction data are routinely recorded in a variety of applications such as retail commerce, bank systems, and website management. Transaction data record the timestamp and relevant reference data needed for a particular transaction record. It is a non-trivial task for a standard ETL to process transaction data with dependencies and high velocity. This chapter presents a two-tiered segmentation approach for transaction data warehousing. The approach uses a so-called two-staging ETL method to process detailed records from operational systems, followed by a dimensional data process to populate the data store with a star or snowflake schema. The proposed approach is an all-in-one solution capable of processing fast/slowly changing data and early/late-arriving data. This chapter evaluates the proposed method, and the results have validated the effectiveness of the proposed approach for processing transaction data.

© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.

A regional analysis of factors influencing electricity consumption is performed using panel dataset. It was identified that climate factors and an economic factor of hotel occupancy could be used to characterise the regional monthly electricity consumption. This study selects Jakarta, Indonesia as the case study, covering the monthly time period from 2006-2012. Multiple linear regression approach is applied to analyse the association between the continues variables. The computation and visualisation is executed in R. One of the explanatory variables, namely hotel room occupancy, is interesting to be analysed in this case because the case study is the capital of the country. Many national events are being held centrally in Jakarta. In practice, limited studies have used this variable to model electricity consumption. The finding shows that the simplest model is conducted by excluding the variable day of rain and variable rainfall that slightly contribute to the model. The result identifies that the relationship between electricity consumption and hotel occupancy is statistically significant, as well as the relationship between electricity consumption and temperature.

High-resolution electricity load profiles are often used for analysis-based decision makings, energy modelling, or other modelling tasks such as household electricity consumption. Smart meters have made it possible to store realtime data, where the data are being optimised by the demand response programs. This paper presents the household load profile method by using smart meter data in Indonesia, which is the case study of this work. An efficient load profile framework is proposed by defining the data selection, applying the interpolation technique and performing clustering of multiresolution time-series data. The clustering through Multiresolution time-series data is a detailed approach to identify the load profile comprehensively at all levels and aspects, include minute, hourly, daily and weekly resolutions in the weekdays and weekend. The mean, maximum, minimum and random patterns are presented in the results. The presented approach of multi-grained load profile analysis may identify the potential of energy savings. The potential of energy savings may support consumers’ energy efficiency by adjusting the timing and amount of electricity use, and the utilities may improve better energy management by shifting the energy consumption from peak to non-peak hours.

In data warehousing, the data from source systems are populated into a central data warehouse (DW) through extraction, transformation and loading (ETL). The standard ETL approach usually uses sequential jobs to process the data with dependencies, such as dimension and fact data. It is a non-trivial task to process the so-called early-/late-arriving data, which arrive out of order. This paper proposes a two-level data staging area method to optimize ETL. The proposed method is an all-in-one solution that supports processing different types of data from operational systems, including early-/late-arriving data, and fast-/slowly-changing data. The introduced additional staging area decouples loading process from data extraction and transformation, which improves ETL flexibility and minimizes intervention to the data warehouse. This paper evaluates the proposed method empirically, which shows that it is more efficient and less intrusive than the standard ETL method.

It is important to understand residential energy use as it is a large energy consumption sector and the potential for change is of great importance for global energy sustainability. A large energy-saving potential and emission reduction potential can be achieved, among others, by understanding energy consumption patterns in more detail. However, existing studies show that it requires many input parameters or disaggregated individual end-uses input data to generate the load profiles. Therefore, we have developed a simplified approach, called weighted proportion (Wepro) model, to synthesise the residential electricity load profile by proportionally matching the city’s main characteristics: Age group, labour force and gender structure with the representative households profiles provided in the load profile generator. The findings indicate that the synthetic load profiles can represent the local electricity consumption characteristics in the case city of Amsterdam based on time variation analyses. The approach is in particular advantageous to tackle the drawbacks of the existing studies and the standard load model used by the utilities. Furthermore, the model is found to be more efficient in the computational process of the residential sector’s load profiles, given the number of households in the city that is represented in the local profile.

Smart meters are the electronic devices for measuring energy consumption in real time. Usually, static public IP (Internet Protocol) addresses are allocated to realize the point-to-point (P2P) communication and remote controlling for smart metering systems. This, however, restricts the wide deployment of smart meters, due to the deficiency of public IP resources. This paper describes the road map architecture development of smart meter controlling system with dynamic IP addresses. We start our development from the naive architecture to a novel subscription-based communication architecture (SCA) for the support of dynamic IP addresses and group controlling of smart meters. The paper evaluates the proposed architecture by comparing the traditional P2P architecture, and validate its effectiveness to interact with smart meters.

A large majority of energy systems models of smart urban energy systems are modelling moderate climate with seasonal variations, such as the European ones. The climate in the tropical region is dominated by very high stable temperatures and high humidity and lacks the moderate climate's seasonality. Furthermore, the smart energy system models tend to focus on CO2 emissions only and lack integrated air pollution modelling of other air pollutants. In this study, an integrated urban energy system for a tropical climate was modelled, including modelling the interactions between power, cooling, gas, mobility and water desalination sectors. Five different large scale storages were modelled, too. The developed linear optimization model further included endogenous decisions about the share of district versus individual cooling, implementation of energy efficiency solutions and implementation of demand response measures in buildings and industry. Six scenarios for the year 2030 were developed in order to present a stepwise increase in energy system integration in a transition to a smart urban energy system in Singapore. The economically best performing scenario had 48% lower socio-economic costs, 68% lower CO2e emissions, 15% higher particulate matter emissions and 2% larger primary energy consumption compared to a business-as-usual case.

User activities play a crucial role in the energy demand of residential buildings and will become increasingly important for smart homes with low energy consumption and low emissions. However, it is often difficult to obtain detailed user activity data and associated energy consumption. User behavior patterns are closely related to energy consumption for modeling or simulation. This paper presents a probabilistic activity model that builds from time-use survey data and creates the data generator that can generate realistic activity sequences for individuals and households. The experimental results demonstrate the effectiveness of the proposed model.In this paper, we focus on the user activity simulation of residential buildings. First, we establish a probabilistic model calibrated by the real-world time-use data with detailed activity information of residents. Then, based on the activity model, we develop a data generator to generate individual and multi-family activity sequences that include eight possible states. In the end, we evaluate the models using statistical methods, and compare with the real-world time-use survey data.

The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also examines the correlation between energy intensity and the characteristics of buildings and occupants, load profiles of households, consumption behavior changes over time, and consumption variability. The results show that the majority of the customers can be represented by fairly constant load profiles. Calendar context has an impact not only on the patterns but also on the consumption intensity and user behaviors. The variability studies show that consumption patterns are serially correlated, the customers with high energy consumption have lower variability, and the consumption is more stable over time. These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.

The pervasive use of Internet of Things and smart meter technologies in smart cities increases the complexity of managing the data, due to their sizes, diversity, and privacy issues. This requires an innovate solution to process and manage the data effectively. This paper presents an elastic private scientific cloud, SciCloud, to tackle these grand challenges. SciCloud provides on-demand computing resource provisions, a scalable data management platform and an in-place data analytics environment to support the scientific research using smart city data.

Air quality monitoring has become an integral part of smart city solutions. This paper presents an air quality monitoring system based on Internet of Things (IoT) technologies, and establishes a cloud-based platform to address the challenges related to IoT data management and processing capabilities, including data collection, storage, analysis, and visualization. In addition, this paper also benchmarks four state-of-the-art database systems to investigate the appropriate technologies for managing large-scale IoT datasets.

Developing countries, especially in Sub-Saharan Africa, face large challenges to achieve universal electrification. Using the case of Ghana, this study explores the role that rice residues can play to help developing countries meet their electrification needs. In Ghana, Levelised Electricity Costs (LEC) of a grid-connected 5 MWe straw combustion plant ranged between 11.6 and 13.0 UScents/kWh, based on region of implementation. Rice straw combustion is a viable grid-connected option in all regions, as the bioenergy Feed-in-Tariff is 29.5 UScents/kWh in Ghana. Residue supply cost contributes significantly (49–54%) to LEC of rice straw combustion. LEC of husk gasification mini-grids ranged between 5 and 53 UScents/kWh for rural populations between 3000 and 250 people. Husk gasification mini-grids can be a suitable electrification solution for these un-electrified populations, as its LEC is lower than the average LEC of grid extension diesel mini-grids and off-grid solar systems for remote communities in Ghana. Electricity produced from husk gasification has the potential to cater to 7% of the needs of un-electrified communities in Ghana. The methodology and analysis of this study can support policymakers of similar countries decide the economic feasibility of decentralised bioenergy solutions while forming national electrification plans.