scholarly journals Linking Dynamic Building Simulation with Long-Term Energy System Planning to Improve Buildings Urban Energy Planning Strategies

Smart Cities ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1242-1265
Author(s):  
Lidia Stermieri ◽  
Chiara Delmastro ◽  
Cristina Becchio ◽  
Stefano Paolo Corgnati
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Clean Energy ◽  
Energy System ◽  
Building Energy ◽  
Building Simulation ◽  
Energy Planning ◽  
Low Grade ◽  
Urban Energy ◽  
The Impact

The building sector is currently responsible of 40% of global final energy consumption, influencing the broader energy system in terms of new electricity and heat capacity additions, as well as distribution infrastructure reinforcement. Current building energy efficiency potential is largely untapped, especially at the local level where retrofit interventions are typically enforced, neglecting their potential synergies with the entire energy system. To improve the understanding of these potential interactions, this paper proposes a methodology that links dynamic building simulation and energy planning tools at the urban scale. At first, a detailed bottom-up analysis was conducted to estimate the current and post-retrofit energy demand of the building stock. The stock analysis is further linked to a broader energy system simulation model to understand the impact of building renovation on the whole urban energy system in terms of cost, greenhouse gas emission, and primary energy consumption up to 2050. The methodology is suited to analyze the relationship between building energy demand reduction potential and clean energy sources’ deployment to shift buildings away from fossil fuels, the key priority for decarbonizing buildings. The methodology was applied to the case study city of Torino, Italy, highlighting the critical role of coupling proper building retrofit intervention with district-level heat generation strategies, such as modern district heating able to exploit low-grade heat. Being able to simulate both demand and supply future alternatives, the methodology provides a robust reference for municipalities and energy suppliers aiming at promoting efficient energy policies and targeted investments.

Urban Building Energy Planning With Space Distribution and Time Dynamic Simulation

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Lin Fu ◽  
Zhonghai Zheng ◽  
Hongfa Di ◽  
Yi Jiang
Keyword(s):  
Energy Demand ◽  
Air Conditioning ◽  
Energy System ◽  
Building Energy ◽  
Space Distribution ◽  
Energy Planning ◽  
Energy Sources ◽  
Simulation Tool ◽  
Time Dynamic ◽  
Urban Energy

It is important to deal with energy saving in buildings of one city level, and plan the energy system from one building to one city level. We strongly suggest conducting urban building energy planning (UBEP) in the urban planning field in China. There are two main characteristics of an urban building energy system. First, the terminal building energy demand is dynamically timely. Second, the energy demand, energy sources supply, energy equipments, and networks of heating, cooling, gas, and electricity, are distributed in an urban space. It is meaningful to conduct an innovative urban energy planning with space distribution and time dynamic simulation. Therefore, an UBEP simulation tool, developed by our research group, is introduced. Finally, a case of energy planning in Beijing City in 2010 for heating and air conditioning system is dynamically simulated and analyzed. To meet the same building energy demand in Beijing, such as heating, air conditioning, gas, and electricity, different energy equipments, such as boiler, combined heating and power, combined cooling, heating, and power system, and heat pump based on different energy sources, such as coal, gas, and electricity, should be planned alternatively. Also, an optimum urban energy system with high energy efficiency and low environmental emission can be achieved. This simulation tool contains most models of heating and cooling energy systems in China. We can validate the models with statistical data from previous or present simulation, and the simulation results in future planning can serve as guidance for the construction of municipal energy infrastructure. We can conclude that simulation in time dimension shows the characteristics of dynamic load in each nodes of the energy flow. The objective is to present the comparison of different scenarios and optimize the planning schemes.

Urban Building Energy Planning With Space Distribution and Time Dynamic Simulation

2008 ◽  
Author(s):  
Lin Fu ◽  
Zhonghai Zheng ◽  
Hongfa Di ◽  
Yi Jiang
Keyword(s):  
Dynamic Simulation ◽  
Energy Demand ◽  
Air Conditioning ◽  
Energy System ◽  
Building Energy ◽  
Space Distribution ◽  
Energy Planning ◽  
Energy Sources ◽  
Time Dynamic ◽  
Urban Energy

It’s important to deal with building energy-saving in one city level and plan the energy system from one building to one city level. It’s suggested strongly to conduct urban building energy planning in urban planning system in China. There are two main characteristics of urban building energy system. That is, firstly, the terminal building energy demand is dynamic timely, such as the heating, cooling, gas and electricity load of 8760 hours a year with peak and valley load. Secondly, the energy demand, energy sources supply, energy equipments and networks of heating, cooling, gas and electricity are distributed in urban space. It’s meaningful to conduct an innovative urban energy planning with space distribution and time dynamic simulation. In this paper, the energy planning method with space and time characteristics is presented and analyzed briefly. In the meanwhile, to meet the same energy demand in buildings, such as heating, air conditioning, gas and electricity, different energy equipments such as boiler, CHP, CCHP and heat pump based on different energy sources such as coal, gas and electricity can be planned and should be alternative among those energy sources and equipments. Thus, a well alternative urban energy system with high energy efficiency and low environmental emission should be simulated. Therefore, an urban building energy planning (UBEP) simulation tool developed by our research group is introduced. And finally, a case of energy planning in Beijing City in 2010 for heating and air conditioning system is simulated dynamically and analyzed.

scholarly journals A new clustering and visualization method to evaluate urban energy planning scenarios

2018 ◽  
Author(s):  
Sara Torabi Moghadam ◽  
Silvia Coccolo ◽  
Guglielmina Mutani ◽  
Patrizia Lombardi ◽  
Jean Louis Scartezzini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Areas ◽  
Energy Demand ◽  
Planning Process ◽  
Energy Transition ◽  
Climatic Conditions ◽  
Energy Planning ◽  
Transition Strategies ◽  
Urban Energy ◽  
The City

The spatial visualization is a very useful tool to help decision-makers in the urban planning process to create future energy transition strategies, implementing energy efficiency and renewable energy technologies in the context of sustainable cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban renovation scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes is proposed, here, to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this paper is to present an innovative solution for the computation and visualization of energy saving at the city scale.The energy demand of cities, as well as the micro-climatic conditions, are calculated by using a simplified 3D model designed as function of the city urban geometrical and physical characteristics. Data are extracted from a GIS database that was used in a previous study. In this paper, we showed how the number of buildings to be simulated can be drastically reduced without affecting the accuracy of the results. This model is then used to evaluate the influence of two set of renovation solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to all cities worldwide with limited amount of information.

scholarly journals Digital Twin-Driven Decision Making and Planning for Energy Consumption

2021 ◽  
Vol 10 (2) ◽  
pp. 37
Author(s):  
Yasmin Fathy ◽  
Mona Jaber ◽  
Zunaira Nadeem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Production ◽  
Energy Use ◽  
Synthetic Data ◽  
Average Energy ◽  
Energy System ◽  
Real Dataset ◽  
Digital Twin ◽  
The Impact

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

An Integrated Energy Strategy for the Optimization of Retrofit Actions in Urban Planning

2014 ◽  
Vol 935 ◽  
pp. 312-315
Author(s):  
Ilaria Falcone ◽  
Michele Grimaldi
Keyword(s):  
Energy Consumption ◽  
Urban Planning ◽  
Energy Demand ◽  
Energy Savings ◽  
Research Work ◽  
Decision Support Tool ◽  
Energy System ◽  
Support Tool ◽  
Energy System Model ◽  
Urban Energy

This research work aims at providing a methodology to analyze quantitatively energy sustainability of existing urban fabric and creating an urban energy system model as a decision support tool for the urban planning processes. Spatially resolved energy demand allows the identification of critical areas of energy consumption (CECA), in particular, a local-type spatial analysis has been adopted, GIS based, using a Kernel density estimation (KDE) and maps algebra. Within the CECA a simulation of energy consumption on an annual base for a representative building was carried out, in order to explore and estimate limits and vulnerabilities and to propose a hierarchy of energy-savings measures, addressing different scales of criticality in urban energy systems, from the city to district and block level.

scholarly journals Analysis and Modeling of Energy Demand System in Iran’s Buildings and the Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 53-62
Author(s):  
Seyed Morteza Emami ◽  
Mehdi Ravanshadnia ◽  
Mahmood Rahimi
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Residential Buildings ◽  
Energy System ◽  
Future Perspective ◽  
Energy Planning ◽  
Reference Scenario ◽  
The Future ◽  
The Status ◽  
Increasing Demand

Abstract In this study, the demand of the Iran’s energy carriers is analyzed and modeled for the country’s largest consumer, buildings and related industries, in the status quo and future perspective. To this objective, the building sector is divided into two sections: household section (residential buildings) and services (business-office and service buildings) according to the ISIC classification that each of these sections is divided into sub-sections. In addition, building-related industries include some non-metallic minerals and basic metals industries. Regarding to scenario-based energy planning helps to increase the understanding of different probabilities in the future. The future outlook for the system is modeled with the horizon of 2035 with the LEAP modeling tool in the “reference” scenario, indicating the sustainability of the existing energy system in the future. The results of modeling indicate an increasing demand for energy as expected that energy demand carriers in buildings and related industries from 559.8 million barrels in 2014 reach up to 1040.6 million barrels of crude oil equivalents in 2035. Therefore, in order to reduce energy consumption, solutions are modeled and analyzed according to the scenarios for “Improvement of energy consumption in buildings and related industries”, then the greenhouse gas emissions and their environmental effects are investigated.

The impact of deep learning–based equipment usage detection on building energy demand estimation

2021 ◽  
pp. 014362442110347
Author(s):  
Shuangyu Wei ◽  
Paige Wenbin Tien ◽  
Yupeng Wu ◽  
John Kaiser Calautit
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Internal Heat ◽  
Building Energy ◽  
Detection Accuracy ◽  
Equipment Usage ◽  
The Impact ◽  
Detection And Recognition

As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

scholarly journals Local Energy Planning Potentialities in Reducing São Paulo’s Inequalities

2021 ◽  
Vol 24 ◽  
Author(s):  
Flávia Mendes de Almeida Collaço ◽  
Célio Bermann
Keyword(s):  
Renewable Energy ◽  
Simulation Model ◽  
Energy Demand ◽  
Ghg Emissions ◽  
Energy System ◽  
Energy Planning ◽  
Local Energy ◽  
Urban Energy ◽  
The City ◽  
Energy Strategies

Abstract This study analyzes the local energy planning (LEP), a set of urban energy strategies and potential scope, for São Paulo from 2014 to 2030. A simulation model is used to quantify the impacts of implementing LEP strategies on the city’s energy system based on three indicators: energy demand, percentage usage of renewable sources, and greenhouse gas (GHG) emissions. The performance of LEP strategies was analyzed for two scenarios: the first reproduces the city policies in force, and the second expands the population’s access to city energy services. Considering the implementation of LEP in the first scenario, the city exhibits a 65% usage of renewable energy and a 43% reduction in GHG emissions in 2030. Furthermore, implementation of the same strategies in the second scenario, also for 2030, results in a 67% usage of renewable energy with a 24% reduction in emissions compared to 2014.

CLIMATE PARAMETERS IN BUILT ENVIRONMENT FROM ENERGETIC POINT OF VIEW – TOWARDS CLIMATE SEVERITY INDEX IN HUNGARY

2013 ◽  
Vol 1 (2) ◽  
pp. 35-42
Author(s):  
Attila Talamon
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
Severity Index ◽  
Energy System ◽  
Building Energy ◽  
Energy Sector ◽  
Point Of View ◽  
Research Activity ◽  
Climate Resilience ◽  
The Impact

Abstract The building energy sector is not immune from the physical impacts of climate change and must adapt. The impacts are more gradual, such as changes to heating and cooling demand. Disruptions to the energy system can also have significant knock-on effects on other critical services. To improve the climate resilience of the building energy system, governments need to design and implement frameworks that encourage prudent adaptation, while the private sector should assess the risks and impacts as part of its investment decisions. Comprehensive studies covering the impact of climate change on the building energy sector are still lacking, though some regional and sector-specific analysis exists. The buildings sector has been examined in more depth than most, with studies finding that temperature increases are expected to boost demand for air conditioning, while fuel consumption for space heating will be reduced. For the follow-up research activity the question has been posed: How can the climate changing trends and the building sector rising energy demand meet in urban environment? As an outlook this present paper can be defined as an ex-ante document in the DENZERO Project towards generating the Hungarian Climate Severity/Energy Index.

scholarly journals Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Design Stage ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Rapid Urbanization ◽  
Substantial Impact ◽  
Urban Heat ◽  
The Impact

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

Sign in / Sign up

Export Citation Format

Share Document