Building energy retrofit index for policy making and decision support at regional and national scales

The vast data collected since the enforcement of building energy labelling in Italy has provided valuable information that is useful for planning the future of building energy efficiency. However, the indicators provided through energy certificates are not suitable to support decisions, which target building energy retrofit in a regional scale. Considering the bias of the energy performance index toward a building's shape, decisions based on this index will favor buildings with a specific geometric characteristics. This study tends to overcome this issue by introducing a new indicator, tailored to rank buildings based on retrofitable characteristics. The proposed framework is validated by a case study, in which a large dataset of office buildings are assigned with the new index. Results indicate that the proposed indicator succeeds to extract a single index, which is representative of all building characteristics subject to energy retrofit. A new labeling procedure is also compared with the conventional classification of buildings. It is observed that the proposed labels properly partitions the dataset, according to buildingsâ\u80\u99 potential to undergo energy retrofit

Application of neural networks for evaluating energy performance certificates of residential buildings

The Energy Performance Building Directive 91 of 2002, mandates Member States of the European Union to enforce energy certification of buildings through local legislation. Among the Italian regions, Lombardy has issued predicted energy performance certificates for buildings since 2007 which accumulate to over one million entries. The current study is an attempt to validate a dataset of energy certificates by benefitting from the magnitude of registered buildings. Considering that manual evaluation of every entry is exhaustive and time consuming, artificial neural network is used as a fast and robust alternative for predicting heat demand indicators. Various combinations of input features are compared for selecting a reliable model. The number of inputs and hidden neurons are also optimized in order to achieve better accuracy. Results show that using 12 variables from an energy certificate is sufficient for estimating the related heat demand indicator. Regarding the stochastic initialization of neural networks, a set of 100 models are trained for obtaining a frequency distribution and confidence interval. Final results indicate that about 95% of entries fall within ±3 confidence intervals. © 2016 Elsevier B.V. All rights reserved

Evaluation of cities’ smartness by means of indicators for small and medium cities and communities: a methodology for Northern Italy

The need to develop policies that improve energy and environmental sustainability as well as technological innovation is the basis for the increase of the smartness of cities around the world. In the European Union, protocols were developed to measure the smartness of cities through indicators. These indicators however are tailored for large cities and do not fit the case of small cities in a satisfactory way. The paper develops a methodology for assessing smartness through indicators that is applicable to small and medium-size cities. The choice of the indicators is consistent with the ISO 37120 standard and it is inspired by the environmental indicators used in the Sustainable Energy Action Plan of the EU. The proposed methodology could be seen as an expansion of Governance strategies already partially adopted by many cities. The methodology is applied to 3 municipalities of northern Italy and the results obtained are discussed in the paper

Hybrid probabilistic-possibilistic treatment of uncertainty in building energy models : a case study of sizing peak cooling loads

International audienceOptimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic-possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data

Data-driven predictive control for demand side management: Theoretical and experimental results

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies.ISSN:0306-2619ISSN:1872-911

A new bond-slip model for NSM FRP systems using cement-based adhesives through artificial neural networks (ANN)

This paper introduced a novel Artificial Neural Networks (ANN)-based bond–slip model for the Near-surface mounted (NSM) FRP system using cement-based adhesives, as an alternative to epoxy adhesives due to their high-temperature resistance and moisture-durability problems, employing experimental data. Therefore, closed-form formulas were presented for key components of the bond-slip law, including maximum bond stress, corresponding slip, fracture energy, and post-peak branch, while taking important factors into account. Compared to available bond-slip laws, this innovative model demonstrates promising potential in predicting the bond behaviour, thereby enabling more efficient and reliable designs for the NSM FRP strengthening applications using cement-based adhesives

architecture-building-systems/CEAforArcGIS: Pre-release 2.2a4 including the installer for planner edition (DO NOT CITE)

This pre-release of the 2.2 version contains the state of master on 2017-04-19 08:33 (UTC) Features include: work on an installer for the planner edition (installs addin for ArcGIS Desktop 10.4

architecture-building-systems/CityEnergyAnalyst: CityEnergyAnalyst v.3.34.2

Enhanced zone helper to store street address [v3.34.2] 19 September 2023, in Zurich & Singapore zone_helper now saves the street addresses and postal codes of the buildings fetched from OpenStreetMap. The enhanced zone_helper also stores house names and residential types (for Singapore HDB Buildings only). The CEA Team # What's Changed Release 3.34.1 by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3380 Update sg_energy_optimization.yml by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3381 correcting typos by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3382 Saving additional info zone helper by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3388 Full Changelog: https://github.com/architecture-building-systems/CityEnergyAnalyst/compare/v.3.34.1...v3.34.

architecture-building-systems/CityEnergyAnalyst: CityEnergyAnalyst v.3.35.0

<h1><strong>Introducing a link to Grasshopper and Batch process Workflow [v3.35.0]</strong></h1> <p><em>16 January 2024, in Zurich & Singapore</em></p> <p>We are publishing this release as our first response to the feedback acquired from the <a href="https://www.cityenergyanalyst.com/event/2023-cea-user-meeting">2023 CEA User Meeting</a> and the <a href="https://www.cityenergyanalyst.com/cea-exemplary-course-series-studio">Integrated Design Project Course</a> at ETH Zurich.</p> <ul> <li>You can now export your design in Grasshopper into a CEA-executable format.</li> <li>It is also possible to customise and automate <em>CEA workflows</em> using a new feature named <em>Batch process Workflow</em> under <em>Tab Utilities</em>. This is an enhanced feature of the pre-set workflows introduced in CEA [v3.34.0].</li> <li>Batch process Workflow also enables you to iterate your customised CEA workflow over all scenarios under the project in one go.</li> <li>Effort to perform a sensitivity analysis (SA) using CEA is reduced due to CEA's link to Grasshopper, Batch process Workflow and <em>Generate samples for SA</em>.</li> <li>We also enhanced CEA's performance on Mac computers powered by the Apple "M" series. For existing CEA for Mac users, please update your CEA following <a href="https://city-energy-analyst.readthedocs.io/en/latest/installation/installation-on-macos.html#update-existing-installation">this guide</a>. For all CEA for Win users, we kindly ask you to uninstall your current CEA and start a fresh installation.</li> </ul> <p>Need tutorials for the new CEA Features? Find them at the <a href="https://www.cityenergyanalyst.com/learning-camp">CEA Learning Camp</a> to be updated by Jan 2024.</p> <p>The CEA Team</p> <h2></h2> <h2>What's Changed</h2> <ul> <li>Release 3.34.2 by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3390</li> <li>updated workflows based on idp feedback by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3392</li> <li>Use temp directory created by python by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3393</li> <li>Update installation-on-macos.rst by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3395</li> <li>Update pythonocc to enable Mac ARM support by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3265</li> <li>Update demand_writers to remove nan values.py by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3402</li> <li>Revert "Update pythonocc to enable Mac ARM support" by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3406</li> <li>Fix network layout script by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3409</li> <li>Update electrical_loads.py by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3411</li> <li>Replace whitespaces with underscore in building name by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3361</li> <li>Fix mac dependencies by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3412</li> <li>Fix network losses by @MatNif in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3413</li> <li>3346 added detail on optimal district energy networks by @MatNif in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3387</li> <li>removing restriction on dhw types by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3403</li> <li>Update load_curve_supply.py by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3419</li> <li>Improving search for optimal CapacityIndicatorVector by @MatNif in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3414</li> <li>Eliminating zero-capacity components by @MatNif in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3433</li> <li>Seperating release to envrionment and grids by @MatNif in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3435</li> <li>Update installation-on-macos.rst by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3436</li> <li>Fixing a typo in the CH database by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3437</li> <li>Update zone_helper.py by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3440</li> <li>fixing inappropriate coordinate system by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3428</li> <li>batch processing and linkage to rhino/grasshopper by @ShiZhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3444</li> <li>Enable Mac ARM support by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3408</li> <li>Add update instructions for macos by @reyery in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3446</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/architecture-building-systems/CityEnergyAnalyst/compare/v3.34.2...v3.35.0</p&gt