Urban Energy Demand Research Articles

City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

Abstract. Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

UBEM calibration method by energy consumption disaggregation using a change-point model

Urban Building Energy Modeling (UBEM) is valuable for analyzing urban energy demand, but uncertainties can create gaps between predictions and actual usage. Calibration often uses utility bill data due to its urban-scale accessibility. However, comparing this data with simulation results for specific energy uses like cooling and heating is challenging. This study introduces a UBEM calibration method using a change-point model to disaggregate energy into heating, cooling, and base load. Applied to 222 office buildings in Seoul, this method provided more appropriate calibration results by considering individual energy consumption characteristics rather than just total consumption. The CVRMSE for base load, heating, and cooling energy decreased compared to uncalibrated simulations. This research highlights the importance of energy disaggregation when calibrating UBEM with utility bill data, offering a more nuanced approach to urban energy modeling.

Quantifying peak heat demand in neighbourhoods: A UBEM approach and its implications for residential heating electrification in the UK—A case study of Newcastle upon Tyne

Developing Urban Building Energy Models (UBEM) to manage heat decarbonisation at the neighbourhood level is both crucial and challenging. Our study addresses key research challenges: quantifying peak heat energy demand and developing reliable and replicable urban energy demand microsimulations. We applied our UBEM approach to a case study area of 228 houses served by the Ridgeway New Low Voltage (LV) electrical substation in Newcastle upon Tyne, to estimate the difference between existing peak heat and electricity demand at this scale. Results show that peak heat demand is 5 to 14 times greater than peak electricity demand, significantly exceeding estimates from national studies. Furthermore, our work follows a comprehensive validation framework, comparing our simulation results against all relevant and available UK datasets. This comparison demonstrates that our model is an overall good fit at the declared Level of Detail. However, our paper identifies significant challenges, particularly in validation, that need to be addressed to improve the reliability and usability of future UBEMs. Finally, we reflect on all our findings and make policy recommendations, as we believe addressing the issues raised in this paper is vital for enabling area-based planning of residential heating electrification, particularly in the UK.

Impact of urban form on building energy consumption in different climate zones of China

Despite numerous studies on the relationship between urban form and building energy demand, understanding the impact mechanisms of urban form is still insufficient. Here, we identified the direct and indirect influences of urban form on building energy demand. The direct influence includes the blocking of solar radiation. The indirect influence is exerted through the urban heat island (UHI) effect as different urban forms produce different UHI intensities. The UHI effect was calculated using the urban weather generator (UWG) tool. Then, the analysis of the direct and indirect influences on the building energy demand was carried out using building energy modeling (BEM) based on the output of UWG. Five cities in different building climate zones in China were investigated, and the corresponding direct and indirect influences were quantified based on the proposed UWG + BEM framework. Results show that warmer temperatures lead to an average increase in space cooling of 16 %–46 % while reduced space heating of 8 %–17 %. The shading of surrounding buildings allows an increase of 7 %–36 % in space heating and a decrease of 14 %–48 % in space cooling compared to that of an isolated building. In addition, direct influence due to shading is prominent in Shuangyashan, Xuzhou, and Fuzhou, accounting for 50 %, 80 %, and 57 % of variations in building energy consumption.

Advancing urban water-energy demand predictions with a rotor hopfield neural network model optimized by contracted thermal exchange optimizer

ABSTRACT The relationship between urban water and energy demand is crucial for resource efficiency, sustainability, and environmental conservation. Rapid urbanization, population growth, and climate change necessitate integrated models that capture the complex interdependencies, feedback loops, and trade-offs between water and energy systems. This research addresses this intricate relationship by developing a modified Rotor Hopfield Neural Network (RHNN) Model using input indicators like population data, GDP, water consumption, precipitation, electricity consumption, wastewater discharge, and industrial coal usage. The model is optimized using a modified metaheuristic, called Contracted Thermal Exchange Optimizer (CTEO), resulting in a comprehensive forecast of urban water-energy demand. The model’s superior efficiency is demonstrated by comparing it with other contemporary methods. Upon comparison with alternative approaches, it is clear that the RHNN/CTEO model surpasses them, showcasing a mean relative error of 1.47% for water usage and 2.60% for energy consumption. This leads to an overall average MRE of 2.03%. This research contributes to the existing body of knowledge by offering an advanced model for urban water-energy demand forecasting, providing valuable insights for policymakers, urban planners, and stakeholders in making informed decisions related to resource allocation, infrastructure development, and sustainable urban development.

Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation

Energy signatures offer a rapid bottom-up energy modelling tool that relates thermal building space heating and cooling demand to ambient temperature. While simplified from a building physics perspective, energy signatures can easily exploit existing buildings’ operational datasets for their calibration, fitting individual buildings or archetypes. To assess their viability in modelling buildings’ heating and cooling demand at urban or larger scales, it is important to assess whether the same building model expressed as an energy signature could be used under different weather conditions, through spatial (location) or temporal (future weather) transfer. As energy demand depends on environmental factors other than temperature, a systematic error quantification from transferring energy signatures across different climatic contexts is required. These errors are assessed by comparing signature-based estimates to EnergyPlus simulations of a single-family home and an office building archetype exposed to current and future climate across eight US climate zones. Energy signatures are confirmed to be a valid approach if sufficient heating and cooling data is used for their identification. On an annual basis, signature-based estimates result in an error of approximately ± 30 % if the signature is spatially transferred, which can exceed 75 % for unsuitable source locations. When energy signatures are temporally transferred, an annual error of only ± 5–10 % is expected. At a daily resolution, the inherent error from applying the energy signature method is significantly larger than the additional error introduced through a spatiotemporal transfer of the energy signature.

A novel entropy-based method for quantifying urban energy demand aggregation: Implications for urban planning and policy

Urban energy demand aggregation (UEDA) is a key aspect of urban sustainability, as it can help to improve the energy efficiency of urban systems and reduce their environmental impacts. However, UEDA is a challenging task, as it involves aggregating heterogeneous and diverse energy demands of individual buildings into a collective demand at a given spatial scale. This paper proposes a novel entropy-based method for UEDA that quantifies the information loss or distortion resulting from this aggregation process. The method also identifies the optimal spatial scale for UEDA that minimizes information loss or distortion, and evaluates the quality and reliability of UEDA results using entropy-based metrics. We apply the method to a case study of Chicago, where we estimate and analyze the energy demand of buildings at 10 spatial scales, ranging from 1.5 km to 15 km, and for different types of energy sources. We calculate the entropy for each spatial scale and energy source, and compare it with building characteristics and ZIP codes. We also assess the quality and reliability of UEDA results using entropy-based metrics, such as information gain ratio and normalized mutual information. Our results show that different spatial scales reveal different patterns and relationships of energy demand, and that choosing an appropriate scale can enhance the accuracy and efficiency of UEDA. Our results also show that there is an optimal spatial scale for UEDA that balances information preservation and reduction, and that this scale may vary depending on the type of energy source and the urban context. Our findings contribute to the field of UEDA and urban sustainability by developing a novel perspective on urban energy dynamics, revealing the complexity and diversity of urban systems, such as population, land use, transportation, and energy demand.

Passive Building Energy Saving: Building Envelope Retrofitting Measures to Reduce Cooling Requirements for a Residential Building in an Arid Climate

In arid climates, a significant portion of the urban peak energy demand is dedicated to cooling and air-conditioning during the summer. The rapid urbanization rates in developing countries, particularly in the Gulf Cooperation Council (GCC), have intensified the pressure on energy resources to meet the indoor comfort needs of residents. As a result, there has been a substantial increase in energy demand, with a 2.3% rise recorded in 2018. Electricity consumption in residential buildings accounted for over 48.6% of the total electricity consumption. The choice of building fabrics used in a residential building can significantly impact the building’s passive performance and carbon footprint. This study aimed to enhance our understanding of how specific fabric details influence cooling energy usage in arid climates. To achieve this, a validation simulation model was initially created as a base case for a residential housing typology in Al Ain, UAE. This was followed by a parametric energy evaluation of various building envelope features. The evaluation was based on the reduction of yearly cooling load energy. The simulation results indicate that incorporating 50 mm of expanded polystyrene insulation into the outside walls significantly reduced energy consumption for cooling requirements in the arid UAE climate. Furthermore, no substantial difference was observed in the various roofing choices, including cool and green roofs, gravels, and sand roofs. Additionally, we concluded that the total solar energy transmittance (g-value) of windows played a more significant role than thermal transmittance (U-value) in reducing solar heat gain within the spaces. These findings should guide strategic decisions on building envelope upgrading for sustainable societies.

DEVELOPMENT OF QGIS PLUGIN FOR URBAN ENERGY SIMULATION USING 3D CITY MODEL AT THE CITY DISTRICT LEVEL

Abstract. In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this, this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand and reducing CO2 emissions in the built environment.

Comparison of building energy performance in three urban sites using field measurements and modelling in Kayseri, Turkiye

Despite the fact that the interrelationships between urban microclimates and energy demand have been recognised, there are not many processes that combine microclimatic boundary conditions to estimate energy consumption in parametric morphological investigations. Therefore, this paper will demonstrate a simple step-by-step methodology to incorporate the effect of urban microclimate on building cooling energy demand in semi-arid climatic areas. In this study, the combination of ENVI-met, Urban Weather Generator (UWG) and Rhino grasshopper are used to investigate the connection between microclimate and energy in the climatic environment of Kayseri. This coupling’s potential is investigated across compact high-rise, midrise and low-rise buildings, focusing on the cooling requirement on the hottest days. The comparative study shows how and to what extent urban geometry, building height in this case, contributes to modifying the magnitude of microclimate impact on building cooling performance.

Correlating the urban microclimate and energy demands in hot climate Contexts: A hybrid review

Correlating the urban microclimate and energy demands in hot climate Contexts: A hybrid review

Research on the Correlations between Spatial Morphological Indices and Carbon Emission during the Operational Stage of Built Environments for Old Communities in Cold Regions

The escalation of the urban population and energy demands has exacerbated the carbon emission intensity at the operational stage of urban old communities. The spatial elements of the built environments comprising building groups, roads and landscape, and the spatial morphology of these elements, are endowed not only with human activities but also impact local microclimates and overall carbon emissions. Nonetheless, little attention has been paid to the correlation mechanism between the spatial morphology of the urban built environments and carbon emissions. In this paper, the aim is to combine carbon emissions simulation and statistical analysis to find the correlation between the spatial morphological indices and carbon emissions and to bridge the gaps. Thus, guided by the principles of urban energy modeling, this research adopts a parametric process of “information model construction–carbon emission simulation–statistical analysis”. First, taking 60 typical samples of an old community in Jinan, China, as objects, morphological indices such as density, texture and layout are analyzed through regression analysis to highlight their impacts on carbon emissions. Then, a carbon emission prediction model based on spatial morphological indices is established and verified. The results show that the floor area ratio (FAR), building coverage ratio (BCR), enclosure degree (ED), shape factor (SF) and average road aspect ratio (AS) have significant impacts on carbon emissions during the operational stage. Among these indices, the FAR and the ED are identified as the pivotal influencers. The findings confirm the important role of spatial morphological design of old communities in cold regions in improving urban carbon reduction potential, and they provide theoretical underpinnings and empirical data as references for urban morphology design formulated within the context of low-carbon objectives.

Three tensions in governing energy demand: A social practice perspective on Nordic urban interventions

Affecting future urban energy demand will play a significant role in mitigating climate emissions. While the literature has amply focused on the outputs of successful urban climate governance, there has been less focus on the organisational challenge that affecting energy demand will represent. By foregrounding the work practices of civil servants engaging with these issues, this study offers new insights into the possibilities and constraints of affecting energy demand. Inspired by work on social practice theory applied to organisations, it maps the tools, materialities and identity-related dynamics that affect civil servants' work. Empirically, the paper builds on interviews, document analysis and field notes taken in ten Nordic cities. The paper finds that the local agency for governing urban demand is contingent on navigating three tensions: (1) broadening the benefits of climate measures beyond carbon emissions, (2) challenging often politically attractive technologies, and the associated range of actors seen as relevant and (3) clarifying the role and responsibility of municipalities in relation to intervening in citizen's energy demand. By identifying the organisational tensions that civil servants face, the paper helps to understand the challenges encountered in advancing a local climate agenda, and the novel forms of agency that organisations can mobilise.

Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale

Buildings account for over a third of end energy demand in many countries worldwide. Modelling this demand accurately marks the first step in producing forecasts that can help optimize energy efficiency as well as ensure stable grid operation, a growing concern with the electrification of heating and transportation demand and proliferation of renewable energy sources. These changes, for instance through large scale deployment of rooftop solar PV systems also mean that urban electricity demand is in a constant state of flux. To address these issues, a very large number of research papers have appeared in recent literature which propose or test different building and urban energy demand or load forecasting techniques, often with conflicting results. Meanwhile, in the same period, the broader field of forecasting has also witnessed tremendous development due to greater data availability, growth in computing power, and algorithmic innovations. As a result, there is little clarity in the energy demand forecasting field on best practices, especially in relation to the creation, evaluation, and utilization of these forecast models. This ten questions paper addresses this shortcoming and provides answers to the most important questions that researchers and practitioners alike need to ask themselves before creating their own demand forecaster, be it at the building or urban level. In addition to summarizing current best practices, the paper also provides an overview of some of the most important challenges related to energy demand forecasting and a principled framework to address them. Additionally, two companion notebooks provide an in-depth tutorial form introduction to load forecasting at both building and urban scale.

The effect of sustainable mobility transition policies on cumulative urban transport emissions and energy demand

The growing urban transport sector presents towns and cities with an escalating challenge in the reduction of their greenhouse gas emissions. Here we assess the effectiveness of several widely considered policy options (electrification, light-weighting, retrofitting, scrapping, regulated manufacturing standards and modal shift) in achieving the transition to sustainable urban mobility in terms of their emissions and energy impact until 2050. Our analysis investigates the severity of actions needed to comply with Paris compliant regional sub-sectoral carbon budgets. We introduce the Urban Transport Policy Model (UTPM) for passenger car fleets and use London as an urban case study to show that current policies are insufficient to meet climate targets. We conclude that, as well as implementation of emission-reducing changes in vehicle design, a rapid and large-scale reduction in car use is necessary to meet stringent carbon budgets and avoid high energy demand. Yet, without increased consensus in sub-national and sectoral carbon budgets, the scale of reduction necessary stays uncertain. Nevertheless, it is certain we need to act urgently and intensively across all policy mechanisms available as well as developing new policy options.

Socio-Economic and Ecological Impact of Charcoal Production in the Fianarantsoa Region, Madagascar

Charcoal, the main domestic fuel used in the city of Fianarantsoa, comes mainly from the forests of Fianarantsoa II. The increase in demand for charcoal in Fianarantsoa puts considerable pressure on the natural woody formations of Fianarantsoa II. The implication of charcoal burning in deforestation depends on the intensity of charcoal production and the respect of the forest regeneration phase. This activity is, however, essential for charcoal makers who live from it as producers or as resellers. It generates a significant income for the rural population, and even for the resellers in urban areas, despite its relatively low cost compared to other fuels such as gas, oil, ethanol, and electricity. Most charcoal burners are also farmers, but the charcoal produced is intended to meet the urban energy demand of Fianarantsoa. This study focuses on the socio-economic and environmental impacts of this logging in order to highlight the solutions available to ensure sustainable development through the cases of Amnbondrona, Antanifotsy, and Ankaranosy. Environmental education and sustainable development as well as the proposal of income-generating alternatives and more ecological fuels would allow the restoration of environmental harmony while ensuring socio-economic balance.

Evaluating the Impact of Urban Microclimate on Buildings’ Heating and Cooling Energy Demand Using a Co-Simulation Approach

The current research proposes an integrated computational method to consider the effect of the urban microclimate and the higher urban air temperatures on the assessment of urban building energy demands on an annual basis. A one-way coupling procedure is established to generate datasets on typical weather years that can capture the particularities of the urban microclimate as a function of their morphological and geometrical characteristics, thus providing a global perspective of the annual building energy performance at a reasonable computational cost. The proposed simulation method, here applied for an energy performance analysis of generic, non-insulated building units located in four different urban sites of Thessaloniki, Greece, is based on the three tools: (a) the ENVI-met v.4 microclimate model, (b) the Meteonorm weather generator and (c) the dynamic BEPS tool EnergyPlus. The obtained simulation results indicate a decrease in the annual heating energy needs of the examined building units of 8.2–11.5% when the effect of urban warming was accounted for, along with a rise in the annual cooling energy needs of between 13.4 and 28.2%, depending on the case study area.

AutoBPS: A tool for urban building energy modeling to support energy efficiency improvement at city-scale

China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation.

Urban transport energy demand model for Riyadh: methodology and a preliminary analysis

ABSTRACT Saudi Arabia intends to reduce GHG emissions by 278 million tons of CO2eq annually by 2030 through Nationally Determined Contribution (NDC) to UNFCCC. Among many policies, mass transit system and transit-oriented development are being developed with the expectation to reduce energy consumption and GHG emissions in Riyadh. To what extent such initiative can reduce energy consumption and GHG emission is an important question. In this paper, a methodology to systematically measure the impact of mass transit and transit-oriented development in Riyadh city on the energy demand has been developed. For Riyadh, a comprehensive travel demand model considering the impact of mass transit and transit-oriented development is still missing. To this end, this paper aims to fill the gap. This methodology considers the state-of-the-art in travel demand analysis and the local context by combining traditional four-step model and activity-based model for modal-shift. This paper describes the methodology and its application for Riyadh by analyzing modal-shift only between car and metro. The results suggest that metro can reduce energy consumption, but the reduction varies with varying accessibility, car, and metro situations. At high urban density and higher car travel cost, we may achieve as high as 13% reduction in fuel demand.

Thermal and energy benefits of rooftop photovoltaic panels in a semi-arid city during an extreme heatwave event

Rooftop photovoltaic panels (RPVPs) implementation is one of the effective strategies to mitigate urban heat island and relieve urban energy demand with renewable energy resources, which is in need, especially during extreme heatwave events. However, the effects of RPVPs on cooling the urban thermal environment and saving energy have not been fully investigated in terms of RPVPs coverage (CV) and conversion efficiency (CE). To address this question, this study conducted a numerical evaluation of RPVPs on the thermal environment, cooling energy consumption (EC) in buildings, and electricity production (EP) during an extreme heatwave event in a semi-arid Chinese city using the WRF model coupled with building effects parameterization and building energy model. Simulations of twelve scenarios with four CVs and three CEs were conducted. The results indicated that RPVPs implementation could generally lower the 2-m air temperature, due to RPVPs’ higher effective albedo and higher emissivity. An increase in RPVPs CV and CE resulted in stronger cooling effects and larger cooling areas, where the RPVPs with a CV of 100 % and a CE of 0.3 could lower the 2-m air temperature by 0.4 – 0.7 °C, and significantly contributed to a decrease of cooling EC by 14.74 %. The EP could offset and even exceed the cooling EC, with the EP/EC reaching 182.61 %, 135.77 %, and 123.37 % in 100 % coverage (CE = 0.3), 75 % coverage (CE = 0.3) and 100 % coverage (CE = 0.2) scenarios, respectively, indicating that RPVPs could potentially generate enough electricity to compensate cooling EC in semi-arid cities. Our findings have practical implications for the RPVPs implementation and the necessity of improving conversion efficiency for better thermal and energy benefits.