On-site Renewable Energy Generation Research Articles

TELECOMMUNICATIONS ENERGY EFFICIENCY: OPTIMIZING NETWORK INFRASTRUCTURE FOR SUSTAINABILITY

The global telecommunications industry is facing significant challenges due to the rapid growth in data traffic and the growing environmental concerns associated with these networks. This paper explores strategies for optimizing network design and operation towards a greener, more sustainable future. Key challenges include the environmental impact of energy consumption, which accounts for 2-3% of global electricity consumption. The paper focuses on optimizing network design and operation, exploring energy-saving techniques and innovations, and revealing advanced network management optimizations. It also explores the transformative role of renewable energy sources like solar, wind, and geothermal power in mitigating the carbon footprint of telecommunications networks. The paper evaluates on-site renewable energy generation and power purchase agreements (PPAs) as integral components of a sustainable energy strategy. The paper highlights the potential of a holistic approach to telecommunications energy efficiency, including deploying energy-efficient hardware, using green data centers equipped with advanced power management techniques, implementing intelligent power management systems, and optimizing network traffic flow. Network management optimization involves regular evaluation and optimization of infrastructure, eliminating redundancy and underutilized equipment, and employing software-defined networking (SDN) and Network Function Virtualization (NFV). Renewable energy integration is also discussed, with on-site renewable energy generation and power purchase agreements emerging as powerful tools in reducing dependence on traditional grids and fossil fuels. The integration of AI, machine learning, and blockchain technology is also explored. By implementing these strategies, the telecommunications industry can contribute significantly to global environmental goals while securing enduring economic benefits. The implications of a sustainable telecommunications infrastructure extend beyond immediate energy savings, encompassing enhanced brand image, cost-effectiveness, and compliance with evolving regulations.
 Keywords: Telecommunications, Energy Efficiency, Network Design, Green Networks, Network Management, Emerging Technologies.

Unveiling willingness to pay for green stadiums: Insights from a choice experiment

Sports stadiums have faced criticism due to their significant environmental footprint, contributing to the exacerbation of climate risks. In this context, green stadiums, developed to reduce resource consumption and promote sustainable practices, are gaining importance. Using a choice experiment with South Korean spectators attending professional sporting events, this study aims to better understand their preferences for diverse attributes comprising green stadiums and how personal and social norms regarding climate risks influence the formation of stadium preferences. Results reveal that respondents place great importance on the implementation of on-site renewable energy generation, showing their strong commitment to sustainability. Furthermore, spectators who support environmental norms are more willing to pay for green stadium features, indicating a growing recognition of climate-related hazards. Findings from the scenario analyses suggest that stadiums with sustainable technologies and practices have the potential to capture significant market share, even with higher ticket prices. These findings provide invaluable insights for stadium operators and policymakers in developing and promoting green stadiums and sustainability initiatives, addressing the urgent issues of climate change.

Strategies for Low CO2-emission Schools in Norway

Regional and national strategies targeting climate change are a driver for reducing greenhouse gas emissions. Municipalities play an important role towards a sustainable society and in restructuring needed to reach the 2030 European Climate targets. They often require that new buildings shall be zero emission and that the building’s carbon footprint shall be reduced compared to a reference building, starting with public buildings. To reach low or zero emission buildings, the production of renewable energy must equal or outweigh the CO2-emissions associated with the building. The energy consumption of the building should be decreased before designing on-site renewable energy generation systems. This is ensured by following a holistic energy design approach: energy conservation, reducing electrical energy, efficient energy management and producing renewable energy. Although Norway has had a focus on low and zero emission buildings, there is a limited number of low CO2-emission schools. These are designed and constructed by various actors. Therefore, the practical experience and knowledge in the industry is scattered and still in development. This paper aims to summarize practical strategies for low CO2-emission schools in Norway. Several case studies were analyzed to identify common traits and interviews were conducted to identify opportunities, challenges and strategies.

Reversible SOFC/SOEC System Development and Demonstration

The OxEon Energy team continues its 30+ year solid oxide fuel cell (SOFC) development history with the design, fabrication, and installation of two reversible solid oxide electrolysis (SOEC)/SOFC demonstration modules (rSOC), at Idaho National Laboratory (INL) and a private, stand-alone microgrid, scheduled for installation and commissioning in early 2023. OxEon’s SOEC/SOFC technology builds on the success of the SOEC stack installed on NASA’s Mars Perseverance Rover that has produced high-purity O2 by electrolyzing Mars atmosphere CO2 nine times to date.OxEon Energy’s technology space integrates cross-sector coupling to produce hydrogen or syngas from SOEC, electricity via SOFC, and transportation fuels from syngas through Fischer-Tropsch synthesis. A low energy plasma reformer provides an alternative approach of producing syngas from low value hydrocarbons. OxEon’s four complementary technologies enable a flexible approach to leveling fluctuating energy from renewables and converting it to accessible, storable, and higher value fuels and chemicals. The reversible SOEC/SOFC systems described in this work demonstrate the opportunity to generate and store H2 fuel as a method to stabilize and capture excess production from renewable or nuclear energy sources.The two demonstration units described in this work integrate OxEon’s reversible SOEC/SOFC stacks with an effective and reliable balance of plant (BOP) system. The high temperature electrolysis (HTE) systems produce hydrogen through electrolysis using solid oxide cell (SOC) technology derived from OxEon’s heritage stack technology and the advancements made during the development of stacks for NASA’s Mars2020 mission.The two demonstration units described in this work use the same modular system design based on 4-stack quad assemblies. The INL system consists of three 4-stack quad assemblies to meet the 30 kW SOEC/ 10 kW SOFC target. OxEon also designed the manifold and plenum assembly to interface with INL’s existing 50 kW test stand and scaled the hot section unit (HSU) to enclose the system. Pressure drop across the system is minimized by supplying even flow to each of the three stack quads, and allows for air delivery in SOFC mode with a blower rather than an air compressor. INL system installation and testing is scheduled for early 2023. A previous 10 kW SOEC system demonstration at INL exceeded project objectives with 14.5 kW system power output, with uniform performance measured from each of 4 stacks.OxEon is scheduled to deliver a 20 kW SOEC/ 10 kW SOFC system to the private microgrid at Stone Edge Farm in early 2023. The system is comprised of 2 quad modules and BOP that will connect with onsite hydrogen storage and renewable energy generation plant. The system will generate hydrogen in SOEC mode using renewable energy supplied by the farm’s solar array. Hydrogen produced in SOEC mode will be compressed and stored by a system designed by HyET Hydrogen B.V. During times of low renewable power generation, the SOFC system will use stored hydrogen to generate power.The Stone Edge Farm system includes two heat exchangers (one for air, one for fuel) that raise the gas feeds to within 50 ⁰C of operating conditions, and minimize pre-heating required for operation. Pre-heating is accomplished with heaters in the HSU enclosure. The feed path is routed to use a portion of the exotherm generated in SOFC mode. The air heat exchanger is oversized for SOEC mode but sized to accommodate the excess flow required for cooling in SOFC mode. The fuel heat exchanger is sized appropriately to deliver H2 in SOFC operation and steam in SOEC operation.Both systems apply mechanical compression to the stacks outside of the HSU enclosure. This design produces greater force than if the springs are enclosed in the hot zone and reduces the insulation envelope size. The end load is applied through a loading rod, an upper load plate, layers of insulation, and an additional outer load plate, placing the springs outside the insulation package that surrounds the hot region where the stacks are located. Low thermal conductivity ceramic rods minimize heat loss through the load transmission path.The materials set used in the rSOC systems uses a scandia-stabilized zirconia electrolyte-supported cell design with nickel-cermet fuel electrode and perovskite air electrodes. Green electrolyte is tape cast, cut, and fired to produce a dense electrolyte of about 250 microns thickness. Electrode inks are applied via screen printing, then fired to form porous electrode layers. Recent advancements in the air side electrode barrier layer, air electrode layers, and fuel electrode catalyst have improved stack performance and stability. Figure 1

Life Cycle Assessment of Current Portuguese Railway and Future Decarbonization Scenarios

Given the current EU decarbonization targets, the railway transport is a key player to boost mobility toward more sustainable transportation, as it is currently the cleanest high-volume mode of locomotion available. However, a study analyzing the life cycle environmental impact of the existing conventional Portuguese railway has never been performed. Aiming to address this research gap, this paper presents an attributional life cycle assessment (LCA) to quantify the environmental impacts of the Portuguese railway infrastructure and rolling stock, using the Douro line case study. Through the LCA methodology, the current setting (using electric and diesel rolling stock) and three scenarios of full-line electrification (considering 2019, 2030, and 2050 electricity mixes) were analyzed for hotspot identification and an outlook on EU-aligned long-term sustainability prospects. In the current scenario, railway operation accounts for 74% of the total carbon footprint, mostly due to the fuel use of diesel trains and the expended electricity of electric train and infrastructure operation. The total electrification of the line and rolling stock can reduce carbon emissions by 38%, 56%, and 63%, if the 2019, 2030, and 2050 electricity mixes are considered, respectively. Further reductions could also be achieved with on-site renewable energy generation and through future low-carbon construction work strategies.

Techno-economic assessment of solar technologies to meet hospitals energy needs

<span lang="EN-US">Hospitals present one of the highest energy consumptions per surface unit, meaning that on-site renewable energy generation and energy efficiency improvements are key to lower hospitals energy demand, external energy dependence and greenhouse gases (GHG) emissions. In this work, the feasibility from the techno-economical point of view of the installation of three solar-based energy generating technologies in hospitals in different climate locations in Europe is addressed. The potential of solar energy technologies to cover the energy needs of the hospitals under study is conducted proposing a novel design and sizing optimization methodology for on-roof installations. The profitability of the different solar-based installations will vary depending on the solar technology output (electrical, thermal or both) and on the type of energy needs of the hospital; but in all cases, profitability is mostly influenced by the price of the current energy source supplying the hospital energy needs. Levelized cost of energy (LCOE) values for on-roof photovoltaic (PV), solar thermal (ST), and photovoltaic-thermal (PV-T) installations obtained are in the range of <br /> 0.028-0.056, 0.051-0.096, and 0.053-0.128 €/kWh, respectively; for locations in latitudes from 37 N (Seville) to 60 N (Oslo) in Europe. Results from this work aim to serve as reference for similar studies in a wide range <br /> of climates.</span>

Machine learning for forecasting a photovoltaic (PV) generation system

To mitigate the carbon print of buildings, they should have on-site renewable energy generation systems to supply energy for the buildings without relying on the national grid. Renewable generation sources rely on weather conditions and are therefore difficult to rely on as the only source of energy. Photovoltaic (PV) is forecasted through machine learning algorithms (MLA), but different methods have varied accuracy and have different training requirements such as more inputs or more data in general. No previous research has concluded an optimal MLA but to better apply them to PV systems, this must be established. To conclude an optimal MLA for a particular application, the dataset and required outputs must be determined, and how they affect the performance of the algorithm must be evaluated. The aim of this work is to compare benchmark MLA's through accuracy and usability for an operational University campus located in central Manchester, in the north of England. The MLA's including random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) have been employed to forecast the PV system. If the power output of the renewables is accurately forecasted, a building management system (BMS) can be equipped to optimise on-site renewable energy generation. To accomplish this, sixty-four MLA models are created in total for forecasting at multiple horizons and dataset sizes which are validated against real-time data. Results in this work revealed that the RF algorithms have the lowest average error of the multiple tests at 32 root mean squared error (RMSE), whereas SVM, LR, and NN showed at 32.3 RMSE, 36.5 RMSE, and 38.9 RMSE respectively. Errors between forecasted and actual results are recorded in RMSE whereas changes in error are shown in mean actual percentage error (MAPE) to show the changes with respect to the original value. No MLA outperforms all others for accuracy and for requiring less data. No previous research is conducted to evaluate the performance of various MLA PV forecasting models through various sized data sets with critical analysis on the results. The comparison of benchmark algorithms when forecasting the PV generation of a local system allows the critical analysis of the models' accuracy and surrounding characteristics.

Industrial processes and the smart grid: overcoming the variability of renewables by using built-in process storage and intelligent control strategies

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives

Applying Energy Justice Principles to Renewable Energy Trading and Allocation in Multi-Unit Buildings

Although rooftop PV panels and battery energy storage systems have been well established for detached residential buildings, there is still a lack of access to the advantages of onsite renewable energy generation and consumption for residents of multi-unit buildings. To understand the effects of developing distributed renewable energy sources for multi-unit buildings, a new fair energy-sharing model in which different groups of residents can gain benefit from the shared energy systems is proposed. Despite the potential benefits of developing renewable technologies in multi-unit buildings, the energy trading and allocation processes in the buildings can be unfair for some groups of residents. Accordingly, this work studies the main principles of energy justice and analyses how these principles can be applied in the energy trading and allocation processes to achieve fair energy sharing. In addition to fairness and justice, the experimental results show that our method increases the sellers’ profit by 59.7–127% and decreases the buyers’ cost by 8–21%, compared to the baseline methods. Moreover, applying the energy justice principles in the proposed sharing models acts as an efficient incentive for the residents of the multi-unit buildings to invest in the shared distributed renewable energy sources.

Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques

The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR.

Modelling and Optimization of Clean and Affordable Electricity Solution for Small-Scale Savings and Credit Cooperatives (SACCOs)

Renewable energy exploitation is among the development strategies set by the government of Rwanda on the roadmap to 2023/2024 universal electricity access and the United Nations plans by 2030. Numerous previous studies on clean energy technologies in Rwanda have mostly focused on households’ usage but there are currently no studies describing the feasibility of clean energy technologies for financial institutions. The skepticism on renewable energy in Africa was previously reported by some personnel. Having realized that most SACCOs (Savings and Credit Co-Operatives) in Rwanda use diesel technology for backup/emergency electricity supply, taking consideration of abundant solar resources in Rwanda, having seen the viability and feasibility studies from other countries of renewable energy for different institutions (financial included); this work uses the HOMER Energy Software and the electricity load profile of a typical SACCO in Rwanda to analyse the affordability and viability of on-site renewable energy generation for SACCO in Rwanda. The results reveal that a solar PV system with storage can be the optimal solution (with levelized cost of electricity (LCOE) of 0.713 $/kWh which is cheaper than 0.73 $/kWh for diesel technology) for SACCOs located in both off-grid areas and grid-connected areas (with 0.041 $/kWh LCOE which is lower than the current electricity tariff in Rwanda). The findings in this work can serve as basic tools/materials for policy drafters in Rwanda on how financial institutions can contribute to climate change mitigation through self-renewable energy exploitation.

An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems

A key issue in energy flexibility assessment is the lack of a scalable practicable approach to quantify and characterise the flexibility of individual residential buildings from an integrated energy system perspective without the need to use complex simulation models. In this study, this problem is addressed by explicitly quantifying the flexibility of multicomponent thermal and electrical systems commonly found in residential buildings based on an ensemble learning framework that consists of four algorithms, namely, random forests, multilayer perceptron neural network, support vector machine, and extreme gradient boosting. The day-ahead and hour-ahead prediction models developed are periodically updated considering dynamic feature selection based on residential occupancy patterns. The proposed methodology utilises synthetic data obtained from a calibrated white-box model of an all-electric residential building for two indicative occupancy profiles. The energy systems evaluated include a heat pump, a photovoltaic system, and a battery unit. The daily flexibility mappings are acquired by applying hourly independent, and consecutive demand response actions for each energy system considered, using suitable energy flexibility indicators. The results show that the ensemble models developed for each target variable outperform each of the constituent machine learning algorithms. Moreover, the storage capacity resulting from harnessing the heat pump downward flexibility demonstrates accurate accuracy with a coefficient of determination equal to 0.979 and 0.968 for day-ahead predictions and 0.998 and 0.978 for day ahead predictions for the two occupancy profiles considered, respectively. This framework can be used by electricity aggregators to evaluate a building portfolio in an end-user-tailored manner or optimally exploit its energy flexibility considering multi-step predictions to shift electricity usage to off-peak times or times of excess onsite renewable energy generation.

Coupled Heat Power Operation of Smart Buildings via Modular Pumped Hydro Storage

Abstract In the United States, building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings’ energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low-grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high-efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground level. This unique design eliminates the geographical limitation associated with the existing state-of-the-art energy storage technologies. It is easy to be scaled for building level, community level, and grid level applications. By using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this nonisothermal process, available thermal energy, e.g., from thermal storage, combined cooling, heat and power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted, and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Wind Booster Optimization for On-Site Energy Generation Using Vertical-Axis Wind Turbines.

Large cities have a significant area of buildings with roofs that are not used most of the time. Vertical-axis wind turbines are suitable for this kind of on-site renewable energy generation. Since wind speeds are not high in these cities, a suitable solution to improve energy generation is to add a Wind Booster. This paper presents a methodology useful for selecting and optimizing the main components of a Wind Booster. As a case of study, we present this methodology in a Wind Booster for a Vertical Axis Wind Turbine (VAWT) that considers the wind flow’s specific behavior in a particular city. The final Wind Booster design is state of the art and makes use of Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques. We experimented with the conditions of Mexico City, obtaining a 35.23% increase in torque with the optimized Wind Booster configuration. The results obtained show the potential of this methodology to improve the performance of this kind of system. Moreover, since wind behavior is very different in each city, our proposal could be beneficial for researchers looking to implement the best possible wind turbine in their locality.

Enhancing PV Self-Consumption through Energy Communities in Heating-Dominated Climates

The European Union, in accordance with its decarbonization objectives, has enacted the Directive (EU) 2018/2001 and subsequently the Directive (EU) 2019/944 that legally recognizes and regulates the formation of citizen energy communities. These are believed to be key enablers for reducing buildings’ carbon footprint by allowing for a wider diffusion of on-site renewable energy generation and by maximizing renewable energy self-consumption. In this study, the benefits of the energy community are assessed through simulations of average Italian buildings of various sizes, different energy efficiency levels, equipped with a photovoltaic system and a heat pump-driven heating system, and located in heating-dominated climates. The work focuses on energy communities both at the apartment scale—i.e., in a multi-family building—and at the building scale—i.e., in a neighborhood. The net energy consumption, the self-consumption, and the self-sufficiency of all the possible energy communities obtainable by combining the different buildings are compared to the baseline case that is represented by the absence of energy sharing between independent building units. The energy community alone at both the building-scale and the neighborhood-scale increases self-consumption by up to 5% and reduces net energy consumption by up to 10%. However, when the energy community is combined with other maximization strategies such as demand-side management and rule-based control, self-consumption can be raised by 15%. These results quantify the lower bound of the achievable self-consumption in energy communities, which, in the rush towards climate neutrality, and in light of these results, could be considered among the solutions for rationalizing the energy consumption of buildings.

On-site renewable energy for industrial buildings in Sri Lanka: a life-cycle cost analysis

ABSTRACT Industrial sector consumes a substantial portion of electrical energy. Building owners persistently believe that combining on-site renewable energy systems to buildings is not cost-effective in Sri Lanka. Therefore, this research aimed to evaluate the use of on-site renewable energy sources for industrial buildings in Sri Lanka. This research carried out 10 expert interviews and 6 case studies. Experts determined that solar and biomass as viable on-site energy generating sources in Sri Lanka. Life-cycle cost was calculated for six case studies representing three case studies for each viable option followed by a sensitivity analysis. Based on the research findings, biomass can effectively manage waste while eliminating the risk of the emission of harmful biomethane. Based on the calculations, the payback period for a solar system varies between 4 and 6 years and biomass system varies between 6 and 7 years. This research explored the potential of generating on-site renewable energy for industrial building. Although the ‘on-site’ energy generation is not widely discussed in industrial buildings, this research study strengthened the possibility of adopting solar and biomass as on-site renewable energy generation sources.

Aerodynamic design optimization of ducted openings through high-rise buildings for wind energy harvesting

On-site renewable energy generation in the built environment can be achieved by incorporating wind turbines in the integral design of buildings. Passages through buildings are considered promising to strengthen the local wind resource availability but information concerning their design and performance is scarce. Therefore, two key design parameters that can enhance the wind energy performance of ducted openings in high-rise buildings are addressed and optimized via CFD simulations: the fillet radius (r) of the opening and the duct diameter (d0). 3D steady RANS simulations are performed and validated with wind tunnel data from the literature. Fillets are shown to suppress flow separation, thereby enhancing the magnitude and uniformity of the wind speed in the duct and reducing the turbulent kinetic energy. With a reference diameter d0 = D, the best-performing configuration has a normalized fillet radius r/d0 = 0.2, which increases the average wind speed in the duct by 65% and the wind power by 354%. Modifying the duct diameter alone has limited influence. However, combining a larger duct diameter d0 = 1.5D with fillets with r/d0 = 0.4, can yield up to 78% increase in average wind speed and 650% in wind power density. Findings indicate that the dimensionless wind speed in the duct (U/U0) scales closely in proportion to the normalized fillet radius (r/d0). With these results, the present study demonstrates the aerodynamic advantage of ducted openings in buildings and identifies relevant design conditions required to improve the wind resource availability for the prospective implementation of wind turbines.

INTEGRATED ARCHITECTURAL AND ENGINEERING DESIGN STRATEGIES FOR A ZERO-ENERGY BUILDING: ILLINOIS INSTITUTE OF TECHNOLOGY’S DESIGN ENTRY FOR THE 2018 U.S. DEPARTMENT OF ENERGY RACE TO ZERO (SOLAR DECATHLON DESIGN CHALLENGE)

ABSTRACT This paper describes the design of InterTech, a zero-energy mixed-use student residence hall, developed in 2018 by an interdisciplinary team of Illinois Institute of Technology (Illinois Tech) students for the U.S. Department of Energy Solar Decathlon Design Challenge, formerly known as Race to Zero. The main focus is the team’s integrated and iterative approach, which blended architectural design and engineering concepts and led to achieving the high-performance goal. InterTech aims to provide an innovative housing solution to Illinois Institute of Technology’s graduate students and their families. Located along State Street in between Illinois Tech’s main campus and downtown Chicago, it offers a mix of living options providing both independence and access to the campus and to the city. In addition to the residential program, the project includes a small grocery/cafe connected to an outdoor public plaza, and an underground garage. Energy modeling was introduced in the early design stages. The potential of on-site renewable energy generation defined the project’s target Energy Use Intensity (EUI) of 37 kBtu/sqft. Several passive and active strategies were implemented to reduce the building’s total energy needs and meet the target EUI. The implementation of energy conservation measures led to a 25% reduction of the building’s cooling load and a 33% reduction of the heating load. A design EUI of 28 kBtu/sqft was calculated, validating that this design met and exceeded the zero-energy goal.

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management to minimize the reliance on the grid and electricity cost, especially during the peak hours. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.

Green building and policy innovation in the US Low-Income Housing Tax Credit programme

ABSTRACT This article analyses the integration of green building with the largest low-income housing production programme in the US and the innovativeness of state housing agencies. Drawing on policy innovation literature, panel data and regression analysis are employed to quantify associations between state-level characteristics and the adoption of green building criteria into the Low-Income Housing Tax Credit (LIHTC) programme. Results show that housing agencies have increasingly adopted green building criteria, and most have identified co-benefits from energy-efficient buildings and smart growth. Despite overall progress, the rate of adoption of green building criteria has decreased, few US states have comprehensive criteria, and many have dropped important criteria, such as on-site renewable energy generation, often due to perceived additional costs to developers and, in some cases, due to perceived progress in building codes. Results are consistent with hypotheses derived from the literature and suggest that the integration of green building with the LIHTC programme is significantly associated with the states’ internal factors, such as public housing agencies’ motivations and resources, and external factors like regional policy diffusion from other states. Future research should explore organization-level factors that affect environmental policy innovation.