Energy Performance Evaluation Research Articles

Evaluation of energy performance and ecological benefit of free-cooling system for data centers in worldwide climates

Water-side economizer (WSE) is an effective free-cooling solution aiming to enhance the natural cooling duration and in turn reduce the cooling-energy consumption of data centers. However, the system's performance is constrained by the limited understanding of its control mechanisms and the influence of local climatic conditions on its free-cooling capabilities. To overcome these limitations, a thermodynamic model of the WSE system with control strategies is provided and its adaptability performance in diverse worldwide locations is systematically evaluated. Modelica-based models of chiller plants and control strategies are firstly developed, and then the energy consumption and ecological benefit of the WSE system are investigated via large-scale simulations in worldwide climates. The performance indicators and application potential of the system are demonstrated through visualization maps. The analysis reveals significant energy savings potential of the WSE system, with the capacity to reduce annual cooling energy consumption of data centers by 4.4–33.7 % in hot to marine zones, and even by a remarkable 62.6 % in plateaus and cold zones. Furthermore, it is observed that every 1 °C increase in outdoor air temperature can reduce energy efficiency ratio by 1.3–8.7 % while simultaneously increase PUE by 0.3–1.0 %. The ecological benefit results revealed that the WSE system can effectively harness larger quantities of stable free-cooling resources in mixed and cool climate zones, exceeding its capabilities in hot and warm regions. These results offer valuable insights for the impact of climate on the energy and ecological benefits of WSE system for data center, which can be helpful to guide the design or retrofit of such cooling systems in specific locations, presenting an immense opportunity to maximize cooling-energy savings.

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10-100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5-1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

Guidelines for data collection on energy performance of higher-education buildings in Egypt: a case study

ABSTRACT Reliable energy analysis of buildings relies heavily on high-quality data leading to proper indicators. Previous studies have highlighted the importance of data quality in analyzing energy usage in residential and non-residential buildings in order to transform declarations to actions, optimise energy efficiency policies and monitor progress and failures in countries. Collected data must adhere to national and international standards for energy performance in buildings. This study aims to provide practical guidelines for effectively collecting and preparing data suitable for evaluating energy performance in Egyptian higher-education (HE) buildings. The guidelines are developed based on a comprehensive case study, considering data availability in typical educational facilities. Architectural and civil engineering drawings, construction specifications, and occupancy details are accessible. However, actual monthly electrical and natural gas consumption data for individual buildings are lacking. To address this, the study proposes the creation of detailed datasheets for each building, encompassing all energy sources and their electrical and power specifications, such as equipment, machinery, and HVAC systems. These datasheets were utilized to calculate energy consumption and energy usage indicators (EUI). The findings demonstrate that the datasheets enable adequate assessment of energy usage in various spaces within educational buildings, including staff rooms, lecture halls, and laboratories. This facilitates the identification of areas in need of targeted energy efficiency improvements. Notably, the study reveals that electricity consumption in the Faculty of Engineering building is significantly influenced by PCs, laboratories, lighting, and air conditioning.

Energy and exergy performance evaluation of a drying coffee beans system using a photovoltaic–direct solar dryer at different drying temperature conditions

This study investigates the impact of various drying temperatures on the performance evaluation of photovoltaic – direct solar dryers in the coffee bean drying process. The use of photovoltaic systems in solar drying provides opportunities for the future to support clean and eco-friendly renewable energy. Three drying temperature conditions were tested, namely 40°C, 45°C, and 50°C, with the optimum temperature obtained at 50°C. During the drying process, the weather appears to be very sunny and cloudless, resulting in an average solar intensity of 974.36 W/m2. Utilising this solar dryer, it takes 12–16 hours for the coffee bean drying process to achieve a moisture content below 12.5% by the SNI 01-2907-2008 standard, starting from an initial moisture content of 45%. The drying and solar collector efficiencies ranged between 0% to 54% and 67.33% to 88.24%, respectively. Energy consumption is directly proportional to drying efficiency, depicted as the energy utilisation ratio (EUR), with the highest EUR being 22.96%. Meanwhile, the exergy flow (inlet, outlet, and loss) is directly proportional to the solar intensity, forming an open downward parabolic curve, with the average exergy efficiencies obtained at temperatures of 40°C, 45°C, and 50°C being 53.95%, 54.72%, and 56.88%, respectively.

Preparation and properties of nano B/NC/AP@F2602 double-layer energetic fiber

The nano-boron powder (n-B) exhibits remarkable efficacy as a combustion catalyst, making it a prime candidate for use in solid propellant metal burners. Nevertheless, throughout the storage phase, the surface of n-B powder is prone to oxidation and substantial agglomeration, resulting in challenges with ignition and incomplete energy dissipation when utilizing boron powder. In this study, we utilized the coaxial electrospinning technique to produce nanofibers comprised of nano-boron /nitrocellulose/ammonium perchlorate@fluororubber (n-B/NC/AP@F2602), with the nano boron uniformly distributed within the core-shell fibers. In the course of combustion, the fibrous composite of n-B/NC/AP@F2602 undergoes an escalation in the rates of energy output and energy release, owing to the constraints imposed by external F2602((C₂H₂F₂)n and (C₃F₆)n). The n-B/NC/AP@F2602 demonstrates a 1.4-fold increase in peak pressure and a 3.6-fold increase in maximum boost rate when compared to the n-B/NC/AP. The energy performance evaluation results reveal that the mean molecular weight (MC) of combusted products from n-B/NC/AP@F2602 is 29.2 g·mol−1, representing a substantial increase compared to that of n-B/NC/AP. The aforementioned findings exemplify that coaxial electrospinning is an exceedingly efficacious technique for enhancing nanostructures, thereby amplifying the reactivity of energetic particles and offering novel insights into their application in energetic materials.

A methodological framework for optimizing the energy consumption of deep neural networks: a case study of a cyber threat detector

AbstractThe growing prevalence of deep neural networks (DNNs) across various fields raises concerns about their increasing energy consumption, especially in large data center applications. Identifying the best combination of optimization techniques to achieve maximum energy efficiency while maintaining system performance is challenging due to the vast number of techniques available, their complex interplay, and the rigorous evaluation required to assess their impact on the model. To address this gap, we propose an open-source methodological framework for the systematic study of the influence of various optimization techniques on diverse tasks and datasets. The goal is to automate experimentation, addressing common pitfalls and inefficiencies of trial and error, saving time, and allowing fair and reliable comparisons. The methodology includes model training, automatic application of optimizations, export of the model to a production-ready format, and pre- and post-optimization energy consumption and performance evaluation at inference time using various batch sizes. As a novelty, the framework provides pre-configured "optimization strategies" for combining state-of-the-art optimization techniques that can be systematically evaluated to determine the most effective strategy based on real-time energy consumption and performance feedback throughout the model life cycle. As an additional novelty, "optimization profiles" allow the selection of the optimal strategy for a specific application, considering user preferences regarding the trade-off between energy efficiency and performance. Validated through an empirical study on a DNN-based cyber threat detector, the framework demonstrates up to 82% reduction in energy consumption during inference with minimal accuracy loss.

Wall-embedded micro heat pump for radiant heating in buildings: Evaluation of energy and thermal comfort performance

This paper presents a novel radiant heating system with a wall-embedded micro heat pump (WEMHP) for residential buildings. The interior surface temperature of the exterior wall-section empowered by the micro heat pump is independently controlled, enabling distributed thermal comfort delivery. The micro heat pump consists of a miniature compressor along with an aluminum roll-bond condenser and evaporator. In contrast to conventional hydronic radiant systems and heat pumps with separate indoor and outdoor units (i.e., split systems), the WEMHP is a “packaged” system that would not require on-site installation and charging of a secondary water loop and circulation pump or refrigerant piping. This could ultimately lead to reduced installation and maintenance costs. In this study, a detailed system model is presented and integrated with a single-zone building model to assess the energy performance of the WEMHP in heating mode. For a designed living room scenario with an external wall WEMHP-to-wall ratio (HPWR) of 0.6 and window-to-wall ratio (WWR) of 0.2, the simulation results show that up to 23 % energy savings compared with a baseline air-to-air heat pump are possible. The results also indicate the HPWR needs to be higher than 0.4 to achieve performance comparable to the baseline. Additionally, experiments were conducted in a laboratory environment in which surface temperatures were controlled to emulate different scenarios for integration of WEMHP technology and to evaluate thermal comfort conditions. The experimental results show that the median operative temperature differences in the indoor environment were within 0.5 °C for various locations while the largest radiant asymmetry was 2.2 °C for the designed room configuration. Moreover, simulations were conducted to evaluate thermal comfort conditions in applications having a smaller HPWR of 0.4 with various values of WWR. Overall, the combined simulation and experimental study shows that the WEMHP has potential to reduce energy use while maintaining thermal comfort at acceptable levels where the HPWR is higher than 0.4.

A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.

Developing a housing stock model for evaluating energy Performance: The case of Jordan

The housing stock is the long-lasting component of the settlement structure. The dwellings make up 72% of buildings and consume around 40% of the primary energy in Jordan. The government in Jordan aims to meet the goals of Paris Agreement by 2050 toward net zero carbon. Considering its importance, this study aims to provide an approach for developing a housing stock model for Jordan to evaluate energy consumption of the residential sector at national level, as a baseline for improving the energy performance of the entire housing stock.A bottom-up approach is used to generate models of the Jordanian housing stock based on available buildings datasets. The generated 21 archetype models are analysed and used to evaluate the energy consumption of the national housing stock. Two archetype models represent the highest contribution, more than 80%, of annual energy use from the entire housing stock. The uncertainty analysis figures out that the 8.3% discrepancy between the baseline results and the measured data, for the entire stock, can be explained by the expected variation in input data.The study can provide policymakers with valuable insights into the energy performance of Jordan’s housing stock and inform the development of effective policies and strategies to reduce energy consumption and promote sustainable development. For example, it can assist in identifying the most effective interventions for refurbishment of different archetypes in the current stock, and the improvements required in new-build dwellings and thereby facilitate the transition to a low-carbon building stock in Jordan.

Enhancing BIM-BEM integration: solutions for efficient data exchange and energy performance assessment

ABSTRACT This paper explores the efficiency of exporting data from Building Information Modeling (BIM) to Building Energy Modeling (BEM) and proposes solutions to encountered problems. The study considers both detached and attached buildings, analyzing the impacts of resolution parameters, and evaluating energy performance based on BIM model. A case-study approach is employed using a residential apartment building, with data exported from Autodesk Revit and imported into DesignBuilder. The results demonstrate the significance of adjusting resolution parameters (the primary parameters that influence the energy analytical model's accuracy and processing time) for efficient data export. Exploring different values leads to improvements in resolving unclosed spaces and enhancing the geometrical model of building elements. Challenges persist in allocating material finishes to walls. By improving and integrating energy performance evaluation through enhanced accuracy and efficiency of data exchange, the findings have significant impacts on stakeholders involved in building design, construction, and operation. Additionally, this process can optimize energy consumption in buildings. Further research is needed to address remaining challenges and improve BIM to BEM systems compatibility including the development of smart mechanisms for energy model creation and resolution parameters refinement. Case studies on different building types and long-term performance monitoring are recommended. This research contributes to the efficiency of exporting data from BIM to BEM interfaces, offering insights for practitioners and guiding future research in BIM-BEM tools integration.

Retrofit building energy performance evaluation using an energy signature-based symbolic hierarchical clustering method

Retrofitting existing buildings is crucial for significantly reducing energy consumption in the building sector. The continuous monitoring and evaluation of retrofit building energy efficiency is necessary to maintain optimal energy performance. This study proposes a novel method for evaluating building energy performance that combines energy signature analysis and hierarchical clustering. Symbolic data were defined by k-means using differences in gradients and y-intercepts before and after retrofitting extracted from energy signatures. Hierarchical clustering was then performed using the symbolic datasets. This symbolic hierarchical clustering method enhances the utility of open data and facilitates rapid decision-making. Additionally, it allows for a simple assessment of energy performance at the city scale. Through implementing this approach in 49 retrofitted buildings in Gangwon-do, South Korea, five types of symbolic data were identified (Types 0–4). Using hierarchical clustering, these buildings were clustered into six groups (Clusters 1–6). Type 3, representing ideal retrofitting outcomes, was observed in Clusters 2, 3, and 4 (73.47 % of all buildings). Conversely, Type 4 symbols, indicating a rebound effect, were observed in Cluster 1 and 2 (6.12 %). These findings provide meaningful information after retrofitting at the regional level, contributing to effective building management during retrofitting.

AutoBPS-Prototype: A web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China

Prototype building energy models are significant in building energy performance evaluation and energy efficiency potential analysis. However, their establishment process is time-consuming and error-prone. This study aims to develop a user-friendly toolkit, AutoBPS-Prototype, to automatically establish prototype building energy models in China. A web-based graphical user interface of AutoBPS-Prototype was designed and implemented. AutoBPS-Prototype only needs to define the climate zone, building type, and standard vintage to generate a default prototype building energy model based on a pre-built library. In addition, it can generate user-defined models through customized important efficiency values. The OpenStudio Standards used to create the U.S. Department of Energy prototype building models based on the ASHRAE 90.1 standards was extended to cover more prototype building energy models in China, including 20 sub-climate zones, 22 building types and four code and standard vintages. The Chinese building standards were used for the efficiency settings. Besides, case studies were conducted. Firstly, 20 prototype building models of four building types in five cities were generated. The credibility of the model results simulated by EnergyPlus was verified through comparison with measured data. Then, the impact of spatiotemporal differences on building energy consumption was analyzed through simulation of the models using different standard vintages in different climate zones. Finally, a baseline and four upgrade scenarios were designed to explore the impact of various upgrades on building energy consumption. This toolkit aims to assist users in conducting rapid research on building energy efficiency improvement. More toolkit features are under development for retrofit analysis, demand response analysis, and photovoltaics system generation estimation.

Investigation of the Performance of Zero Energy Homes in Hot Climates

Globally, the rate at which buildings use resources and energy has been rising. Due to population growth, rise in living standard, and global warming, strategies for energy conservation have been adopted lately in zero energy buildings. Over the past decade, the energy consumption in constructions has increased at an unprecedented rate. Therefore, it is of great importance to mandate energy rationalization in building construction. This research investigates the impact of applying zero energy concept in homes in Egypt. The study begins by analysing the current situation of the energy sector and existing buildings in Egypt, the NZEB concept is defined and its different aspects are highlighted. Energy simulation by DesignBuilder simulation software is used to evaluate energy performance for a residential single family house model after applying Passive and Active design strategies. A number of energy saving strategies have been examined to optimize the building envelope with minimum energy requirements. The building energy consumption was reduced when various energy efficient strategies were combined. Besides utilizing energy efficient HVAC systems. a solar PV system is installed to meet the reduced energy. The case study was transformed into a zero-energy house. Results show that passive and active techniques can be dependable for steady energy production in buildings in hot dry regions.

UAV-based automated 3D modeling framework using deep learning for building energy modeling

Many approaches to the Urban Building Energy Model (UBEM) have been developed to analyze urban-scale energy demand patterns. The main goal of UBEM is to minimize manual work and improve modeling accuracy when building 3D modeling procedures that deal with vast amounts of data. To do so, it is important to build an automated process and increase the modeling efficiency throughout the process. This study proposes a new framework for automatically generating 3D models for building energy modeling. This framework collects geographic coordinate system (GCS) data by applying algorithms based on a convolutional neural network (CNN), Haversine formula, unmanned aerial vehicle (UAV), and geographic information system (GIS) information. The collected GCS data were used to generate a 3D model using EnergyPlus, resulting in a 3D model capable of a Level of Detail 3. Subsequently, we compared and verified the size and energy performance of the actual building with those of the generated model. The model size errors generated without drawing information are as follows: buildings, 3.69%, windows1 16.75%, and windows2 19.43%. The error range evidenced through the energy performance evaluation indicator showed that the MBE values for the cooling and heating energies were 5.54% and 5.77%, respectively.

Evaluation of energy performance in positive energy building: X HOUSE at Solar Decathlon Middle East 2021

Internationally known as the “Olympics of Sustainable Building”, Solar Decathlon Middle East 2021 is the first competition linked to a world EXPO. The competition requests teams to design, build and operate a zero-energy house to drive the buildings and cities in the Middle East to transform into sustainable and intelligent ones.X HOUSE took first place in the Solar Decathlon Middle East 2021 and won eight contests. This article analyzes how the simulation optimization guides the design of X HOUSE to achieve higher energy efficiency and comfort conditions in terms of passive and active design strategies and solar energy utilization. It verifies the effectiveness of the simulation by a detailed analysis based on the monitored data at the competition site in Dubai. It also studies how house management strategies can improve the actual energy performance of X HOUSE. The analysis results show that the X HOUSE is a Positive Energy Building with highly efficient comfort and energy performance, it has strong power accommodation ability for peak shaving without relying on the grid, and it provides satisfactory thermal, ventilation, luminous, acoustics and healthy environment as well as a fully functional and flexible space. It provides a reference and methodology for the design of PEBs and sustainable development in the Middle East.

Energy Performance Review of Battery-Powered Drones for Search and Rescue (SAR) Operations

Drones and other aerial vehicles are commonly used in Search and Rescue (SAR) Operations to locate, stabilise, and extract distressed individuals. A drone is essentially a flying robot that can be remotely controlled or fly autonomously via software-controlled flight plans in their embedded systems, in conjunction with onboard sensors and a global positioning system (GPS). There are a variety of applications for drones today, and their use is expanding globally. It is a safe bet that the use of drones will continue to increase as technology continues to develop. Constantly raised by drone players is the issue of limited flight time due to poor battery life. Consequently, the purpose of this paper is to investigate the most recent research on the energy performance evaluation of battery-powered drones for search and rescue (SAR) operations.

A simulation-based evaluation of using PCMs in buildings for energy efficiency under different climate conditions

Today, majority of energy consumed in buildings is used to provide thermal comfort conditions. Reducing heating and cooling loads to provide comfort conditions in buildings is possible by taking the right passive design decisions concerning the variables that affect these loads. The building envelope which seperates indoor environment from the outdoor environment and affects the development of indoor thermal comfort conditions is the most important determining factor for energy loads in buildings. In this study, after applying PCM on the building envelope and inner walls and inner floors annual heating, cooling and total energy consumptions and indoor environment comfort conditions in the zones were evaluated. The study was done for Istanbul representing temperate humid climatic zone and for Diyarbakir representing hot-dry climatic zone in the same degree-day region according to the TS825 standards. The residential building simulated for calculations is a 3 storey building with a rectangle floor plan and flat roof and the dimensions of these buildings are 10 m × 30 m × 9 m. The building comprises 9 zones and the building coverage is 300 m2. In order to reduce the difference in energy consumption and thermal comfort conditions among the zones due to their different locations, PCM was applied on inner walls and inner floors in addition to the building envelope to develop a range of alternatives. Annual energy performance evaluation and calculation of daily operative temperature values for January 21 which represents the heating period and July 21 which represents the cooling period were done by the EnergyPlus simulation tool. According to the calculations, the total energy load of the building in the A4.2 alternative where PCM was applied on the building envelope and on inner walls and inner floors was reduced by 11.96 % and 9.69 % in Istanbul and Diyarbakır respectively. With the alternatives, differences in energy consumption among the zones were reduced but not eliminated. Operative temperature values in the zones changed by 1.04 °C – 3.32 °C in Istanbul and by 0.87 °C – 1.16 °C in Diyarbakır with the PCM application contributing to the improvement of indoor comfort conditions.

A Review and Analysis of Green Energy and the Environmental Policies in South Asia

This paper explores the challenges and opportunities for green energy and environment transition in South Asia, a region that faces the dilemma of meeting its growing energy demand while reducing its greenhouse gas emissions and environmental vulnerability. The region has rich renewable energy sources and potential for energy efficiency improvement, but it also relies heavily on fossil fuels and suffers from various barriers and constraints that hinder its green energy development. The region needs policies that can achieve economic growth, social welfare, and environmental sustainability coherently and effectively. Utilizing the thematic literature review approach, this paper examines the literature on four main topics: (1) the estimation of green energy resources potential and scenarios in South Asia; (2) the comparison of green energy targets and policies in the South Asian Association for Regional Cooperation (SAARC) countries; (3) the evaluation of green energy deployment and performance in different sectors; and (4) the identification of green energy transition challenges and opportunities in South Asia. This paper fills some research gaps in the literature by providing a comprehensive, comparative, holistic, and integrated analysis of green energy and environment policies in South Asia, using various data sources, methods, frameworks, criteria, indicators, scenarios, impacts, trade-offs, drivers, barriers, best practices, lessons learned, and policy recommendations. This paper also develops a conceptual model for the green energy transition in South Asia, which consists of five key variables: green energy potential, green energy policies, green energy deployment, green energy performance, and green energy transition. The main findings and implications of this paper are that South Asia has a huge opportunity to pursue a green energy and environment transition that can address its multiple challenges and aspirations, but this requires overcoming various obstacles and constraints that hinder its progress. This paper suggests some policy options and strategies to enhance the green energy and environment policies in South Asia, such as developing a clear and consistent policy framework, enhancing regional cooperation and collaboration, leveraging information technology and data analytics, emphasizing sustainability and resilience, and engaging with other stakeholders and partners.

Synapps - platform for estimating, controlling and optimizing wastewater treatment plants

Wastewater generated by the world's vast population is an important source of pollution and can accelerate the loss of biodiversity and impede the achievement of objectives set by the international community regarding the good status of water bodies. In line with the European Union's sustainable development strategy, which foresees the adoption of increasingly demanding environmental control, energy efficiency and rational management of resources measures, a project with high potential for economic valorization is being implemented, focused on design, development and validation in real conditions, to create an innovative platform for estimating, controlling and optimizing wastewater treatment plants (WWTP), called SYNAPPS. Based on the implementation of multiparametric measurement chains and the application of computational intelligence techniques (eg Big Data Analytics, Data Mining and Machine Learning), the SYNAPPS platform should be capable of providing integrated management of the various WWTP treatment processes, ensuring a high environmental, energy and operational performance, and also simplify and relieve the burden of operating this type of technical infrastructure. SYNAPPS is an R&D project approved by PO Centro, through ANI, started in January 2021 and will last for a period of 30 months, with a budget of around one million euros executed under the responsibility of the consortium formed by CTGA, a company with more than 25 years of experience in the operationalization and management of WWTP and leads the consortium, and by the non-business entities of SI&I, namely, ITeCons with relevant experience in the development of automation and dynamic control systems and in the evaluation of energy and environmental performance of processes, and ISR specializes in evaluating the energy performance of complex systems and developing control algorithms based on computational intelligence.

Solar neighborhoods: the impact of urban layout on a large-scale solar strategies application

The article addresses the challenges of evaluating energy performance in different neighborhood settings under various energy efficiency measures and proposes a methodology for selecting appropriate solar strategies on a neighborhood scale. The study selects five representative neighborhoods from various climatic zones with different building and street layouts. The proposed methodology involves a systematic three-step multi-domain workflow for implementing energy efficiency measures and solar strategies in the existing neighborhoods. The first step involves typical energy performance simulation, the second step involves energy simulation using high performance building envelope, and the third step involves the addition of solar strategies in combination with retrofitting materials to achieve net-zero status. The results of the study show that modifying the building envelope leads to a significant reduction in energy consumption, with up to 60% reduction observed. The study also finds that the optimal mix of solar strategies depends strongly on the type of neighborhood, its street layouts, and the type of buildings. The article highlights the importance of considering these factors when implementing solar strategies on a neighborhood scale to achieve energy efficiency and net-zero status. It provides urban planners with a systematic decision-making approach to evaluate and optimize neighborhoods to achieve net-zero energy status.