An integrated spatial analysis computer environment for urban-building energy in cities

In this paper, we developed a new integrated analysis environment in order to thoroughly analyses urban-building energy patterns, named IUBEA (integrated urban building energy analysis), which focuses on energy modeling and analysis of a city’s building stock to support district or city-scale efficiency programs. It is argued that cities and towns account for more than two-thirds of world energy consumption. Thus, this paper explores techniques to integrate a spatial analysis environment in the field of urban building energy assessment in cites to make full use of current spatial data relevant to urban-building energy consumption and energy efficiency policies. We illustrate how multi-scale sampling and analysis for energy consumption and simulate the energy-saving scenarios by taking as an example of Greater London. In the final part, is an application of an agent-based model (ABM) in IUBEA regarding behavioral and economic characteristics of building stocks in the context of building energy efficiency. This paper first describes the basic concept for this integrated spatial analysis environment IUBEA. Then, this paper discusses the main functions for this new environment in detail. The research serves a new paradigm of the multi-scale integrated analysis that can lead to an efficient energy model, which contributes the body of knowledge of energy modeling beyond the single building scale. Findings also proved that ABM is a feasible tool to tackle intellectual challenges in energy modeling. The final adoption example of Greater London demonstrated that the integrated analysis environment as a feasible tool for building energy consumption have unique advantages and wide applicability.

A new workflow for detailed urban scale building energy modeling using spatial joining of attributes for archetype selection

Traditionally, building energy model is created in isolation from the architectural building information model and energy analyses have relied on a single analysis tool. The building energy model can be generated more quickly by leveraging existing data from the BIM. The impacts of energy consumption are significant in the building usage phase, which can last several decades. Due to the large share of the final energy consumption in the building sector, accurate analysis of thermal and cooling loads of a building and the efforts to reduce energy losses represent an effective way to reduce energy consumption. Therefore, it is essential to analyze the building energy performance in the design phase, which is when critical decisions are made. This study aims to investigate the impact of the building components and construction materials on building energy efficiency using Building Information Modeling (BIM) technology in a mild climate zone. After reviewing the proposed designs, the main building form was chosen for energy modeling and analysis. Then, building energy consumption analysis was performed based on the basic parameters of the building energy model. Eventually, the most optimal mode was selected by examining different energy consumption forms. This study showed that the building HVAC system always had the largest share of energy consumption. Finally, the results of parametric studies on alternative schemes of energy use intensity optimization showed that 22.59 % savings could be achieved as compared to the base building model in a 30-year time horizon

With the rapid acceleration of urbanization and the continuous growth of the urban population, urban energy consumption is projected to rise even more. Quantitative analysis of urban building energy is not only conducive to the formulation of relevant policies and evaluation standards, but also can promote energy conservation and emission reduction in urban buildings. However, current urban building energy simulation faces challenges in obtaining detailed building data. Especially, the complexity and diversity of building types, along with the large number of buildings, making it difficult to know the situation of each building type in urban areas. This paper combines spatial analysis methods with the XGBoost (eXtreme Gradient Boosting) model, utilizing GIS data such as building footprint, POI (Point of Interest), AOI (Area of Interest), and land use data, to achieve the identification and classification of 86.83% of buildings in Shenzhen, with an identification accuracy rate of 84.17%. Based on this, the study establishes 18 types of typical building prototype models and conducts energy consumption simulation and energy-saving potential assessment for buildings in Shenzhen, providing a scientific basis for energy conservation and carbon reduction in urban buildings. The research results indicate that among four energy-saving measures, high-performance window retrofitting has the most significant energy-saving effect, with an energy-saving rate of 6.2%; the energy-saving effects of improving the energy efficiency of air conditioning systems and replacing energy-saving lighting are equivalent, with energy-saving rates of 4.82% and 4.14%, respectively; whereas green roofs have a relatively smaller effect on building energy conservation, with an energy-saving rate of 1.27%.

Building energy renovation is one of the pillars upon which the 2050 European low-carbon goals are based. Simultaneously, building energy renovation is widely recognized as the trump card for the new start of European economy. However, at present the renovation rate of the existing building is very low throughout Europe (approximately 1%) and investments in high performing buildings are generally mistrusted by stakeholders, due to their high capital costs. In this context, this PhD thesis dedicated its efforts to investigate from the energy and financial perspective the consequences of buildings renovation in the European scene. Particularly, the research boundaries were delineated by focusing on non-residential, multi-functional buildings, that are nowadays poorly studied due to their heterogeneous nature. In this view, the thesis' contributions were addressed at three levels: a) multi-functional buildings as archetypes to input in energy models for long-term energy analysis; b) multi-functional buildings used to test the financial viability of energy efficiency projects, in view of reaching the nearly Zero Energy performance level. As these analyses necessarily require case studies, the attention was directed towards a specific type of multi-functional buildings, hotels; c) multi-functional buildings as test-bed to assess the impact of co-benefits on the financial performances of energy efficiency projects. Once again, hotel buildings were selected for the development of the detailed analyses. To include archetypes of multi-functional buildings in bottom-up building energy models, a new modelling method was proposed. The method provides a rationale for the classification of energy end-uses into typical and extra, so that the modeling problem is simplified and a coherent use of well-established Reference Buildings modelling methods is allowed. Then, the focus of the research was narrowed to the hotel sector, which was found to lack of reliable energy performance benchmarks and effective performance-based greens labels. Case study buildings were object of energy and financial evaluations. On one side, real hotels were analyzed to test the application of the EU imposed cost-optimal methodology as a support tool to guide private investors' investment decisions. On the other side, an Italian Reference Hotel was modelled and the cost-optimal methodology was applied to investigate the existing energy and financial gaps between cost-optimal and Nearly Zero Energy performance level in Italy. From both perspectives, findings converged to similar conclusions: high performing retrofit are not financially viable, if avoided energy costs are the only operational benefits accounted for. Starting from these outcomes, the thesis investigated how valuation procedures could be exploited to make NZEB retrofit solutions appealing for private investors. Based on a literature review of the co-benefits of energy efficiency projects, 2 different strategies were pursued and tested on the Italian Reference Hotel. The first approach proposed to monetize co-benefits of energy efficiency interventions based on literature and to include them in the well-established cost-optimal methodology. Results highlighted that co-benefits related to the market appreciation of a retrofitted hotel can drastically change the perception of the financial convenience of an ambitious retrofit project. In the latter strategy, the issue of monetizing non-energy benefits was faced directly: a technique to value non-market goods was applied to monetize comfort. Findings proved that hotels guests' willingness to pay for comfortable indoor conditions is higher than the hoteliers' extra costs for providing them. Due to the context-dependent nature of co-benefits, the findings of the 2 applications do not represent generally applicable quantitative benchmarks. Nonetheless, they confirm the leading role that literature attribute to co-benefits in the success of energy efficiency projects.

Analysis of household energy consumption in Ibadan Metropolis of Nigeria

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

Impact of urban wind environment on urban building energy: A review of mechanisms and modeling

Sensitivity analysis of household factors and energy consumption in residential houses: A multi-dimensional hybrid approach using energy monitoring and modeling

Times series forecasting for urban building energy consumption based on graph convolutional network

Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun, Hong Kong

Improving accuracy in building energy simulation via evaluating occupant behaviors: A case study in Hong Kong

The Indonesian government has proposed energy conservation and renewable energy as the main strategies to decarbonise the electricity sector. Energy conservation is expected to reduce 17% of total energy consumption at business as usual projections by 2025 while renewable energy is anticipated to grow from 20.3 million tonnes of oil equivalent (MTOE) in 2015 to 92.2 MTOE by 2025. Nevertheless, the implementation of these strategies has been hindered by recent unexpected policy changes. In 2017, the country withdrew its energy conservation regulations to solve financial problems caused by low electricity demand growths and overinvestment in new power plants. Similarly, the premium feed-in tariffs (FIT) for pulling up renewable energy investments had been slashed to provide tariffs that are lower than the fossil-fuelled electricity generation costs. This was a direct response to low electricity tariffs resulting from solar farm auctions in Dubai and electricity subsidy constraints. These circumstances forced a reboot of clean energy policies in Indonesia.This research was undertaken to seek and define more effective and efficient clean-energy policy options using a diverse methodology approach consisting of a systematic literature review, time-series analysis, renewable energy projection evaluations, and hybrid energy models for distributed and centralised renewable energy systems. The systematic literature review discussed various renewable energy policies that are globally practised in on- and off-grid systems. The review also identified structural differences between developed and developing countries and the implications of using analytical tools for developing countries. The autoregressive distributed lag (ARDL) model, a time-series analysis, is used to understand the nexus of electricity demands, incomes and electricity prices in the residential, industrial, and commercial sectors. In addition, urbanisation and the number of electricity customers (representing dynamic transitions in developing countries) were used as control variables.Regarding renewable energy, their projection errors in developed countries were analysed to comprehend the most achievable renewables target and inaccurate assumptions applied in the projections. Thereafter, two hybrid energy models were developed which combined techno-economic analysis (TEA), input-output analysis (IOA) and life-cycle analysis (LCA) to assess the impact of proposed policies to the economy and natural environment. The first model, called the Agent-based Renewables model for Integrated Sustainable Energy (ARISE), used socioeconomic data to estimate photovoltaic (PV) market potential emerged from alternative policy interventions. The second model, Power Generator – Agent-Based Modelling (PowerGen-ABM), included linear programming (LP) approach to optimise power plant expansions in 15 main electricity systems under emission reduction targets. PowerGen-ABM uses the results of the ARDL estimations and the evaluations of renewable energy projections.The findings suggested several policy implications on electricity demand and supply sides. The ARDL estimations revealed that electricity demands in all sectors during the period from 1969 to 2015 were significantly affected by income and urbanisation. This finding provided an opportunity for controlling electricity demand growth by mitigating urbanisation effects, e.g., higher electrical appliance ownership. Evaluation of the renewable energy projections concluded that solar energy has the lowest level of uncertainty as it has the most reachable capacity projections. However, other renewable energies entail policies that are more effective, and further researches are needed for the advancement of reliable technology and accurate weather predictions. This thesis also provided ranges for the projection uncertainties of six renewable energy technologies, drawing attention to ways that the dominant errors in these renewable energy projections may be rectified. The results of the ARISE simulations indicated that it would be beneficial to scrap the PV donor gift policy for rural households without electricity access, and instead to improve the production efficiency of the PV industry and establish after-sales services and rural financing institutions. Net metering is the most effective policy for encouraging those in urban areas to invest in PV in a climate where fossil energy prices are on the rise while PV prices are dropping. Lastly, the PowerGen-ABM simulations recommended the utilisations of geothermal, large hydro, micro-hydro, and wind energy as these are the most cost-effective technologies that can be used to meet emission reduction targets.This research offered two contributions to existing literature in renewable energy systems and modelling. The ARDL estimations addressed the limitations of previous studies, which could not verify co-integrated relationships between electricity demand, income and electricity prices in Indonesia. Moreover, to the best of author’s knowledge, ARISE is the first energy model that integrates the triple bottom line of the economy - energy - environment (E3) to social behaviour analysis. PowerGen-ABM is also the first energy model that implements linear programming and E3 in the agent-based modelling (ABM) framework for analysing optimal expansions of power plants. As a pioneering model, ARISE and PowerGen-ABM have potentials to benefit from further improvements and integration of ARISE to PowerGen-ABM.

In this paper, a review of the current literature in modeling urban heat island (UHI) phenomena including its main causes and effects is summarized. The main goal of the review is to assess the current modeling capabilities to accurately determine the impacts of UHI on outdoor comfort levels and urban building energy demands. In particular, the analysis techniques and modeling approaches are overviewed to estimate the mutual thermal interactions between urban atmosphere and buildings. In addition, the applications and the limitations of various modeling methods are discussed to predict outdoor thermal comfort and urban building energy consumption. The specific capabilities of the reviewed modeling approaches are highlighted to assess the effectiveness of mitigation strategies of the UHI effects. As part of the review analysis, recommendations are outlined to improve current modeling approaches to predict more accurately the impacts of UHI phenomena on urban building energy performance.

The construction of buildings and their operation contribute to a large proportion of total energy end-use worldwide; indeed, buildings account for 40% of the total energy consumption and for 36% of CO2 emissions in the European Union. The sector is expanding, which is bound to increase its energy consumption. In order to reduce the growing energy expenditure, the European Directive imposes the adoption of measures to improve the energy efficiency in buildings. The recast of the Directive on the Energy Performance of Buildings defined all new buildings will be nearly zero-energy buildings by the end of 2020. However, the transformation of the EU's building stock will not be completed until well after 2020 and this target can only constitute an intermediate step. Indeed, the recent Commission Roadmap for moving towards a competitive, low-carbon economy showed that emissions in the building sector could be reduced by around 90% by 2050. While new buildings should be designed as intelligent low or zero-energy buildings, refurbishment of existing building stock has many challenges and opportunities because, in the building sector, most energy is consumed by existing buildings. Since the replacement rate of existing buildings by the new-build is only around 1-3% per annum, a rapid enhancement of taking up retrofit measures on a large scale is essential for a timely reduction in global energy use and promotion of environmental sustainability. Consequently, defining minimum energy performance requirements for new and, in particular, for existing buildings represent a key element in European building codes. For this reason, EPBD recast has set out Member States must ensure that minimum energy performance requirements are set with a view to achieve cost-optimal levels for buildings, building units and buildings elements. A cost-optimal level is defined as the energy performance level which leads to the lowest cost during the estimated economic lifecycle. It must be calculated in accordance with a comparative methodology framework that is based on the global cost method. To apply this methodology Member States are expected to define a series of Reference Buildings as baseline and representative models of the national building stock. Additionally, they must define energy efficiency measures to be applied to Reference Building; these ones can be a single measure or constitute a package of measures. Reference Buildings can be exploited as a basis for analysing national building stock and the potential impacts of energy efficiency measures in order to select effective strategies for upgrading existing buildings. Finally, once estimated the Reference Building energy consumptions and the impact of the different energy efficiency measures, the costs of the different packages are estimated in order to establish which of them has the lowest global cost and, consequently, represents the cost-optimal level. Global cost method considers the initial investment, the sum of the annual costs for every year and the final value, all with reference to the starting year of the calculation period. A measure or package of measures is cost-effective when the cost of implementation is lower than the value of the benefits that result, taken over the expected life of the measure. The cost-optimal result represents that retrofit action or combination of actions that minimized the global cost. From the variety of specific results, a cost curve can be derived; the lowest part of this curve represents the economic optimum for the specific set of the analyzed energy efficiency measures. This PhD study deals with complex scenario above described. Its main objective is to examine cost-optimal analysis in order to establish if this methodology can be an appropriate tool to guide and support decisions related with buildings energy performances. In detail, a critical review of the methodology has been developed and some sensitivity analyses have been exploited in order to testing the robustness of the cost-optimal analysis results. Considering the influence that similar outcomes could have on the European energy policies and on the roadmap towards 2050, it is fundamental to evaluate, even before the same outcomes, how these are reliable. Cost-optimality as a theoretical concept is well and clearly established. However, its application is far from easy and straightforward. Indeed, cost-optimal analysis is a complex methodology characterized by an inherent degree of uncertainty in the final outputs; choices of methodology, procedural decision and complexity of much of the input data significantly affect outcomes. In addition, the research highlights that often although a cost-optimal calculation is being developed and some energy efficiency retrofit measures are individualized, there are no effective instruments, in term of energy policies and financial tools, to drive the market to increase the rate of deep renovations