Annual Heating Demand Research Articles

Uncertainty and Risk Evaluation of Deep Geothermal Energy Source for Heat Production and Electricity Generation in Remote Northern Regions

The Canadian off-grid communities heavily rely on fossil fuels. This unsustainable energetic framework needs to change, and deep geothermal energy can play an important role. However, limited data availability is one of the challenges to face when evaluating such resources in remote areas. Thus, a first-order assessment of the geothermal energy source is, therefore, needed to trigger interest for further development in northern communities. This is the scope of the present work. Shallow subsurface data and outcrop samples treated as subsurface analogs were used to infer the deep geothermal potential beneath the community of Kuujjuaq (Nunavik, Canada). 2D heat conduction models with time-varying upper boundary condition reproducing climate events were used to simulate the subsurface temperature distribution. The available thermal energy was inferred with the volume method. Monte Carlo-based sensitivity analyses were carried out to determine the main geological and technical uncertainties on the deep geothermal potential and risk analysis to forecast future energy production. The results obtained, although speculative, suggest that the old Canadian Shield beneath Kuujjuaq host potential to fulfill the community’s annual average heating demand of 37 GWh. Hence, deep geothermal energy can be a promising solution to support the energy transition of remote northern communities.

Building heat consumption and heat demand assessment, characterization, and mapping on a regional scale: A case study of the Walloon building stock in Belgium

Energy consumption in buildings results in CO2 emissions and it is necessary to reduce energy consumption thus its related emissions. This research is included in the Wal-e-cities project, which is funded by the European Regional Development Fund (ERDF) and aims to create tools that facilitate the transition toward smart territory. The annual heat consumption (HC) and heat demand (HD) of Wallonia building stock of more than 1,700,000 buildings are assessed. Subsequently, the developed energy models are coupled with a geographic information system (GIS) to calculate and map the HC and HD. The HC and HD are calculated for each building and are represented by different levels of territorial aggregation, namely neighbourhood, municipality, and urban region scales. The highest HC values were observed in large cities and main industrial areas, whereas the lowest values were observed in rural areas. For residential sector, HC is mainly related to the number of dwellings, which differs from that of tertiary and industrial sectors where HC also depends on the nature and function of buildings. Based on mean values at the neighbourhood scale, the HD is 16.44% lower than the HC for the residential sector, 15.78% lower than the HC for the tertiary sector, and 9.26% lower than the HC for the industrial sector. The proposed energy models are validated. The relative differences between annual HC calculated in this study and that provided in the regional energy reports are −5.82% for the residential sector, −14.29% for the tertiary sector, and −2.02% for the industrial sector.

Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones

Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of different building designs in seconds. Recent studies have shown the diverse benefits for sustainable building design. Surrogates were applied to provide rapid feedback at the early design stage, to accelerate sensitivity analysis, uncertainty analysis and design optimization, or to improve building model calibration.However, the current process of surrogate modelling offers much room for improvement. In particular, a surrogate model is bound to the specific building design problem it has been trained for. This includes a specific site, requiring time-intensive retraining if the building performance at another location is to be analysed.In this paper, we develop a single surrogate model that spans arbitrarily many locations. For that purpose, we are among the first to use a deep temporal convolutional neural network to process annual multivariate weather data with hourly resolution (≈150,000 inputs). The network learns features relevant to estimate heating or cooling demand. We combine these location-specific weather features with building design parameters to serve as input to a single surrogate model (feed-forward neural network). In a case study with 569 weather files from locations in Canada, we show that the surrogate model deviates by less than 3% when predicting annual heating demand for new building designs at locations outside of the training data set.

An Innovative Façade Element with Controlled Solar-Thermal Collector and Storage

A novel façade element is presented that forms a symbiosis between an enhanced box-type window, a closed cavity façade, and a Trombe wall. This hybrid, transparent-opaque façade element features an absorbing water tank, that is installed behind a controlled shading device toward the cavity of a non-ventilated Double Skin Façade in the parapet section. To evaluate the potential impact on building performance, a transient simulation model is developed in Modelica and calibrated by comparison with measurements on a prototype. The effect of the absorbing thermal storage on heat transfers under solar radiation is analyzed in comparison to (i) conditions excluding solar radiation and (ii) an empty tank. An evaluation for four European cities demonstrates that the annual heating demand can be reduced by more than 4.2% and cooling demand by at least 6.6% compared to a façade without thermal storage. The effect is explained not only by the increased thermal mass, but also by the effective modulation of solar gains by the controlled absorbing storage. The dampening of heat flow fluctuations and the control of solar gains is a promising means to reduce the installed power of HVAC (heating/ventilating/air conditioning) installations.

Design of an innovative PV/T and heat pump system for greenhouse heating

The very high annual heat demand of greenhouses is the most critical factor that increases production costs. Conventional methods are generally used to obtain the optimum temperature required for greenhouses. In these systems, greenhouse air is heated by a boiler and pipe networks are connected to it, and in this way, most of the heat energy is transferred from the greenhouse ceiling to the atmosphere. In addition, in the greenhouse, not only the air but also the soil should be heated in order not to spoil the roots of the plants. The objective of this research is to provide sustainable heating for greenhouse applications. For this purpose, an innovative heating system has been designed for greenhouse heating by using of solar energy and heat pump technologies. In this study, a new approach was presented by designing a novelty heat pump flow for the heat required in the greenhouse. With this design, not only greenhouse air but also the soil will be heated and the best conditions for the development of plants will be provided. In the system, an ethylene glycol water mixture was used to prevent damage caused by freezing. In addition, it is designed to provide sustainability with an auxiliary heater when solar radiation is insufficient. It is highly recommended to apply this presented system for all greenhouse types.

Production of district heat, electricity and/or biomotor fuels in renewable-based energy systems

We analyze the possibilities to produce district heat, electricity and/or biomotor fuels in renewable-based energy systems considering district heating systems with annual heat demands between 110 and 1000 GWh. Several technical configurations to co/polygenerate district heat, electricity and/or biomotor fuels are cost optimized in the context of different standalone production options, woody biomass fuel prices and integrating costs of intermittent energy resources. The cost optimum configuration varies with the scale of the district heat production, as does the use of biomass to generate the same amount of product, and both depend on the context of the overall energy system. There are several state-of-the-art technologies that offer significant co/polygeneration benefits within district heating systems. An electric-based solution for transportation as well as an electric-based option for small-scale district heating systems are cost efficient and will reduce the use of biomass and help to integrate wind electricity into the overall energy system. For larger district heating systems, cogeneration of heat and electricity, and also in combination with electric heat pumps for a high biomass price, are cost efficient solutions. Further studies are needed, for example to understand how the deep energy renovation of buildings may influence the configuration of co-/polygeneration systems in district heating systems and potential savings of woody biomass.

On the design of an urban data and modeling platform and its application to urban district analyses

An integrated urban platform is the essential software infrastructure for smart, sustainable and resilient city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimize smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.The paper discusses software architecture concepts for data and modeling urban platforms, which allow to analyze and optimize the urban infrastructure with their energy, water and further resources such as food or goods consumption. Building, commerce and industry as well as the transport sector are in the focus of the efficiency and renewable supply analysis. The main driver is to derive zero carbon strategies for cities while including all major sectors of CO2 generation.So far, two software architecture concepts have been implemented and tested, both using a 3D CityGML geometry data model: A workflow management system for city scale building energy modeling using a monthly energy balance calculation method and a micro service orchestration for dynamic building simulation modeling. The GIS based data analysis methodology and building energy workflow modeling method are applied to a case study district in New York City to demonstrate the implementation status and derive CO2 mitigation strategies. The results showed how data gathered from different sources for the relevant sectors can be translated into CO2-emissions. For a district in Brooklyn connected to a large electric substation with electricity monitoring data available, every individual building´s heating and cooling demand was simulated and resulted in a total annual heating demand of 1.72 TWh/a and 0.11 TWh/a of cooling. It could be shown that the building sector cooling demand can by reduced by 63% by a change of set point temperature for cooling and reduced infiltration rate, while reducing the heat demand by 12%. In addition, carbon accounting of the food and wastewater sector was done using the same GIS based modeling framework. The analysis showed that the food related electricity consumption for refrigeration corresponds to about 5% of the overall electricity consumption and requires 2.6 GWh/a for transport. Using food waste in co-digestion of wastewater treatment plants could contribute another 7.4 GWh/a of combined heat and power. Such quantification of demand and energy sources could contribute to prioritize actions for CO2 mitigation strategies in urban areas.

Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector

Scarcity of affordable energy efficient dwellings is a defining characteristic of the global housing crisis. In many countries this problem has been exacerbated by single objective cost-models which favour the homogeneous development of market tenures at the expense of delivering high-quality affordable homes. Despite the obvious environmental and fuel-poverty alleviation benefits of advanced energy performance standards, such as Passivhaus, they are often dismissed as an affordable housing solution due to elevated build-cost premiums. The present work attempts to reconcile this housing affordability – energy performance nexus by establishing a novel decision support framework for Passivhaus design using genetic multi-objective optimization. The use of constrained genetic algorithms coupled to the Passive House Planning Package software is shown to produce cost optimal designs which are fully compliant with the Passivhaus standard. The findings also reveal that the precise choice of Passivhaus certification criteria has significant impacts on overheating risks using future probabilistic climate data. This means that the design implications of using either the peak heating load or annual heating demand certification criteria must be temporally evaluated to ensure resilient whole-life design outcomes. In a typical UK context, the findings show that affordable Passivhaus dwelling construction costs can be reduced by up to £366/m2 (or 22% of build cost). Use of this evidence-based decision support tool could thereby enable local authorities and developers to make better-informed decisions in relation to cost optimal trade-offs between achieving advanced energy performance standards and the viability of large affordable housing developments.

Implications of supplying district heat to a new urban residential area in Sweden

Here we analyze the cost, primary energy and CO2 implications of supplying district heat, considering different supply and return temperatures, to a new residential area in Växjö, Sweden. We explore four land exploitation alternatives consisting different types of buildings with various heated floor areas as well as occupancy densities and two levels of building energy efficiency, based on the Swedish building code and passive house criteria. The analysis shows that energy performance of the buildings and land exploitation alternatives strongly influence the annual heat demand and its profile, which steers the design of the local heat distribution network. Additionally, supply and return temperatures of district heat somewhat influence the design of the network. The distribution heat losses could be reduced by 25% and 50% if district heating systems of 65/30 °C and 50/20 °C, respectively, are used instead of a conventional 80/40 °C system. However, for the same land exploitation alternative, the local distribution heat losses are about the same whether or not the buildings are designed to meet the Swedish building code or passive house criteria, since the same pipe distance is required and the variation of pipe diameters is small. A 50/20 °C system increases electricity use to boost hot water temperature to avoid the risk of legionella bacteria, and this influences quantity of district heat supply, primary energy use and costs. Therefore, a 65/30 °C system appears to be more primary energy and cost efficient than a 50/20 °C system. Increased insulation of district heating network reduces heat losses but this is not cost effective due to increased investment cost. The results are similar whether or not the analysis is based on current energy supply or future renewable based energy supply. This study increases understanding of strategies for planning and designing new urban residential areas and their energy supply systems to reduce primary energy use as well as monetary costs, and to minimize the climate impacts of the built environment.

Reuse Waste Material and Carbon Dioxide Emissions to Save Energy and Approach Sustainable Lightweight Portable Shelters

Abstract Despite their great significance, lightweight structures have poor thermal inertia. In order to enhance the thermal comfort inside such buildings, architects need lightweight thermal storage. In this paper a model was used to experimentally investigate Heating Load profiles in lightweight shelters. The profiles were created for the climate in Jordan, then simulated for other climate zones. The proposed design concept was used to create a replacement for a thermal mass in lightweight structures such as shelters; by combining passive solar gain with energy storage embodied within the shelter floor (thermal-floor) to absorb solar radiation. This shelter design decreased the Heating Load during the winter season by acting as heat storage that releases energy at night time after being exposed to solar radiation during the day. The passive design depends on shading elements and overhangs shades to control solar gain during different seasons to prevent overheating during the summer. An experimental investigation of this model was performed to validate the simulation results. Validated simulation results showed that the designed thermal-floor is 25 % of the total shelter’s floor area, which was crucial for obtaining favourable results. With CO2 as a thermal mass, heat load was reduced up to 68 % compared to a 20 cm concrete slab floor. The use of this thermal storage material yielded a reduction in annual heating demand by 85 kWh/m2.

Heat Supply Comparison in a Single-Family House with Radiator and Floor Heating Systems

Floor heating and radiators are two of the most common types of hydronic heating systems used for space heating in single-family houses in cold climate regions. Notwithstanding, there are few comparative studies on indoor temperature distribution and system cost evaluations for radiators and floor heating. Furthermore, there are no aligned outcomes in terms of total heat supply for a single-family house with radiators or floor heating. In this study, the effect of building energy efficiency level and construction type, including flooring material, on the supply heating demand and transmission heat losses were studied for both radiator and floor heating systems. For this purpose, a single-family house located in Växjö, Sweden, was modeled as a case study. The heating demand was supplied with a district heating system with a similar supply temperature at 45 °C for both the radiator and floor heating system. A sensitivity analysis was also performed to assess the effect of flooring configurations on the annual supply heating demand for both conventional and passive versions of the case-study building. The results showed that the radiator-integrated building had a lower supply heating demand in comparison with the floor heating-integrated buildings. Based on the sensitivity studies, the flooring material did not have a significant influence on the supply heating demand and on the transmission heat losses in the case of the radiators. The supply heating demand was only reduced up to 3% if the flooring U-value was improved by 60%. The results also showed that refurbishment in a standard conventional building with a radiator heating system based on the passive criteria led to a 58% annual energy savings, while this amount for a building with a floor heating system was approximately 49%.

A building clustering approach for urban energy simulations

Within the context of amongst others urban energy planning and energy system design, urban and district energy simulations have gained interest to quantify the energy use of existing districts. To reduce calculation time and in absence of adequate detailed building level data, urban energy simulations often deploy reductive modelling approaches based on a limited set of buildings, or archetype buildings. This may lead to significant modelling errors when the archetype buildings are not tailored to the studied location. This paper explores a building clustering approach that harvests available local building information, e.g. geospatial data, to generate a tailored set of archetype buildings. Focussed on simulating the annual heat demand or peak heat demand, this paper evaluates if clustering on building properties can be an alternative to clustering on the energy key performance indicators of interest to define the tailored archetypes. As consumption data on a building level is often not available, such an approach would eliminate the need to simulate the energy use for all buildings. The results show that indeed clustering on the properties is a viable alternative with robust results for both annual energy use and peak energy demand and a comparable accuracy compared to clustering on the targeted performance indicators.

Impact of neighbourhood-scale climate characteristics on building heating demand and night ventilation cooling potential

As buildings are main contributor to greenhouse gas emissions, it is important to assess the performance of existing buildings and assist the design of new sustainable buildings through building energy simulation. It is well known that by using local climate measurements for building energy simulation would provide more accurate result than by using other typical weather data, i.e. typical meteorological year (TMY). However, as different built forms/architectural layouts would also have impacts on neighbourhood-scale microclimate, it is worthy to quantify the difference it would make. In this study, we performed a year-long measurement with four weather stations surrounding a campus building in 2009 and 2010. Each station was placed in a typical type of built form, including a street canyon, a courtyard, a semi-closed courtyard and a relatively larger open area. Besides, two typical weather data files, typical meteorological year (TMY) and actual meteorological year (AMY) were taken as reference. Annual heating demand and natural ventilation cooling potential were calculated based on all 6 weather files. Our simulation results show that the variation in annual heating demand of different built forms could be between 1.1 and 7.3%, where the large open area has the highest heating demand and it of the courtyard is the lowest. The difference between on-site measurement and TMY in annual heating load is as high as 10.8%. While in summer, night ventilation cooling potential of the courtyard and the semi-closed form are the highest, and it of the street canyon is the lowest. Using TMY could underestimate the night ventilation cooling potential by 26–31% and using AMY could overestimate it by 9–14% in total. Overall speaking, the courtyard form shows good performance in reducing heating demand and enhancing night ventilation cooling, while the street canyon shows relatively poor performance in both aspects. These findings highlight the importance to understand the impact of neighbourhood-scale microclimate on building energy performance.

Coupled hydraulic and mechanical model of surface uplift due to mine water rebound: implications for mine water heating and cooling schemes

In order to establish sustainable heat loading (heat removal and storage) in abandoned flooded mine workings it is important to understand the geomechanical impact of the cyclical heat loading caused by fluid injection and extraction. This is particularly important where significantly more thermal loading is planned than naturally occurs. A simple calculation shows that the sustainable geothermal heat flux from abandoned coal mines can provide less than a tenth of Scotland's annual domestic heating demand. Any heat removal greater than the natural heat flux will lead to heat mining unless heat storage options are also considered. Thematic collection: This article is part of the SJG Collection on Early-Career Research available at: https://www.lyellcollection.org/cc/SJG-early-career-research

Operation Algorithms and Computational Simulation of Physical Cooling and Heat Recovery for Indoor Space Conditioning. A Case Study for a Hydro Power Plant in Lugano, Switzerland

This article presents the computational simulation process and the operation algorithms of the VAV and VRV systems, for indoor space conditioning, with extensive physical cooling and heat recovery. Through the introduction of appropriate operation algorithms, the article aims to highlight the high energy saving potential on indoor space conditioning, by exploiting physical cooling and heat recovery processes. The proposed algorithms are evaluated with a case study for a hydro power plant building located in the area of Lugano, Switzerland, with significant cooling needs for the whole year, due to high internal heat gains from indoor electrical equipment. This fact enables physical cooling during winter, for the cooling load coverage, and heat recovery, for the concurrent heating load coverage in different thermal zones of the building. Analytical operation algorithms are developed for a VAV and a VRV system. Both algorithms are computationally simulated. With the VAV system, 86.1% and 63.7% of the annual cooling and heating demand, respectively are covered by physical cooling and heat recovery. With the VRV system, 58.5% of the annual heating demand is covered by heat recovery. The set-up cost of the VAV system is almost twice higher than the set-up cost of the VRV system.

Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes

Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%.

Optimization of the hydraulic performance and integration of a heat storage in the geothermal and waste-to-energy district heating system of Ferrara

District heating is an effective way to increase the share of renewables in the thermal energy sector. The district heating network (DHN) of the municipality of Ferrara in northern Italy uses a geothermal reservoir at a depth of 1 km supplying hot water at 100 °C. Besides geothermal energy, the overall annual thermal demand of 170 GWh is covered by a combined heat and power (CHP) plant fed by municipal solid waste (MSW), and by auxiliary natural gas boilers. A short-term thermal energy storage having a useful capacity of 45 MWh decouples the heat production plants from the distribution network. An advanced monitoring and control system has been recently implemented to supply a high-level service to the customers connected to the network, searching for the maximum electricity saving by reducing the supply pressure of the pumping station, and targeting the maximum natural gas saving by a proper production programming of the heat supply plants. The paper describes the main design and operational features of the district heating system, and shows how the improved operation was successful in reducing the electricity and natural gas consumption. The new pressure management consists in monitoring the differential pressure in many substations, especially those located at the periphery of the network, and adjusting the water supply pressure in order to fulfil the minimum differential pressure at the most critical substation. The modified production scheduling consists in keeping using the geothermal energy and waste-to-energy plants in periods of low thermal demand and low electricity prices to daily charge the storage system. Experimental data measured in the first months of improved operation show a high saving potential of electric power for pumping equal to 400 MWh/year, which represents a saving of 30% compared to the previous “manual” optimization of the hydraulic performance. Moreover, the enhanced storage operation enables a saving of 7000 MWh/year of energy from natural gas, which finally contributes for only 11% to the annual heat demand. The calculated payback time of only a few years shows the high cost saving potential of these energy conservation measures in such systems.

Impact of exterior convective heat transfer coefficient models on the energy demand prediction of buildings with different geometry

Accurate models for exterior convective heat transfer coefficients (CHTC) are important for predicting building energy demand. A detailed review of the literature indicates that existing CHTC models take into account the impact of building geometry either incompletely, or not at all. To the best of our knowledge, research on the impact of exterior CHTC models on the predicted energy performance of buildings with different geometry has not yet been performed. This paper, therefore, investigates the influence of CHTC models on the calculated energy demand of buildings with varying geometry. Building energy simulations are performed for three groups: buildings with Hb (building height) > Wb (building width), buildings with Hb < Wb and buildings with Hb = Wb. Six commonly used CHTC models and a new generalized CHTC model are considered. The generalized CHTC model is expressed as a function of Hb and Wb. The simulations are performed for low and high thermal resistances of the building envelope. The results show that the different CHTC models provide significantly different predictions for the building energy demand. While for annual heating demand, deviations of −14.5% are found, for the annual cooling demand a maximum deviation of +42.0% is obtained, compared to the generalized CHTC model. This study underlines the need for the CHTC models to consider building geometry in their expressions, especially for high-rise buildings. For low-rise builgings, the observed deviations between the existing and the generalized CHTC model are less pronounced.

Assessing the Energy Performance of Solar Thermal Energy for Heat Production in Urban Areas: A Case Study

One of the major challenges faced in the optimization process of existing heating systems is the harnessing and integration of a higher share of renewable energy. Centralized integration at the thermic source leads to high investment costs at the first investment stage, and low values of conversion, transport, and storage efficiencies, due to high levels of heat loss from large-capacity entropic systems. This research paper presents a case study on the partially decentralized integration of thermal solar fields that are used for heat production in crowded urban areas within the optimization process of the existing heating system in the city of Oradea, Romania. A deterministic method was used as the method for the calculation of heat demand, in both stationary—hourly heat demand and dynamic regimes—annual heat demand, and the simulations within the configuration and the optimization process of the hybrid heating systems were carried out. In the case study, four representative urban areas with different thermal densities were analyzed within two working scenarios, which took into account the energy performances of the buildings inside the studied areas before thermal retrofitting, and after a usual thermal retrofit.

Building Blocks Energy Estimation (BBEE): A method for building energy estimation on district level

In this paper, a new method entitled “BBEE (Building Blocks Energy Estimation)” for estimating energy performance at the district level is proposed. It consists of two branches, namely the BBEE algorithm and an energy database of a typical zone. This method is used to keep architects and urban planners informed of the energy performance of their design by clustering zones and looking up corresponding energy intensities from the database. Because the related energy databases are preexisting, energy performance modeling and simulation are not required from the user. On the other hand, the database is set up and continuously replenished by parameterization simulations with a basic model of typical zones. This task was separated from the estimation process and operated by experts, which shortened the process and reduced modeling errors. As a case study, an urban district with eight buildings was estimated under three climate conditions separately following the proposed BBEE method. Results were compared with the full-model simulation using a simulation tool called DeST. The difference in annual heating demand between estimation and simulation was reported as less than 6% in all three cases. The potential applications and limitations of the proposed BBEE method are discussed at the end of this paper. Although further improvements are still required, the new estimation method could be helpful for energy performance estimation at the district level at the early stage of design.