Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Erratum to: Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Erratum to BUILD SIMUL(2020)13:1281–1290 DOI 10.1007/s12273-022-0961-5 The name of the third author Jérôme Kämpf was incorrectly listed as Jerome Kämpf in the original online version of the article.The article has been updated with the correct name.The author name of Jérôme Kämpf in the Author contribution statement section should also be corrected.The Author contribution statement with the correct author names is as below.

Effects of a structurally related substance on the crystallization of paracetamol

Paracetamol （PCM） was crystallized from an isopropanol （IPA） solution containing various small amounts of metacetamol as an additive. The effect on the nucleation kinetics was studied by measuring the induction time to nucleation and the metastable zone width using focused beam reflectance measurements （FBRM） and attenuated total reflectance （ATR-UV/Vis） spectroscopy. Both the induction time and the metastable zone width were expressed as functions of the additive concentration. Small amounts ofmetacetamol （1-4 tool-%） were found to cause significant inhibition to the nucleation by extending both the induction time and the metastable zone width. A progressive change in the morphology of the paracetamol crystals from tabular to columnar habit was observed with increasing metacetamol concentration. The solvent also had a significant effect on the size of the paracetamol crystals as smaller crystals were obtained in IPA than in aqueous solution. The dissolution rate of paracetamol was improved by the incorporation of metacetamol with 4 tool-% having the most effect. A supersaturation control （SSC） approach was implemented for the PCM-IPA system with and without metacetamol in an attempt to control and obtain larger metacetamol-doped paraeetamol crystals.

Real-world challenges for multi-agent reinforcement learning ingrid-interactive buildings

Building upon prior research that highlighted the need for standardizing environments for building controlresearch, and inspired by recently introduced challenges for real life reinforcement learning (RL) control, herewe propose a non-exhaustive set of nine real world challenges for RL control in grid-interactive buildings(GIBs). We argue that research in this area should be expressed in this framework in addition to providing astandardized environment for repeatability. Advanced controllers such as model predictive control (MPC) andRL control have both advantages and disadvantages that prevent them from being implemented in real worldproblems. Comparisons between the two are rare, and often biased. By focusing on the challenges, we caninvestigate the performance of the controllers under a variety of situations and generate a fair comparison.