Applying modeling and optimization tools to existing city quarters

Prieto, Mario Potente; Uribarri, Pilar Monsalvete Álvarez de; Tardioli, Giovanni

doi:10.1016/b978-0-12-811553-4.00010-x

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…In the literature, it is generally accepted that the data set is divided into 70% or 80% training, 30% or 20% test in determining the training and test data (Prieto, et al, 2019) (Vasques, et al, 2016) (Brownlee, 2020) (Zou & Qu, 2020. In this study, to obtain the most relevant results from the data set, training rates in different combinations were tried.…”

Section: The Data Set and Methods Of The Studymentioning

confidence: 99%

Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Temür¹,

Yıldız²

2021

ibr

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This study aims to create a monthly sales quantity budget by making use of the previous income data of an enterprise operating within the construction sector, which is considered the locomotive of the economy. For estimating time-series of sales as a linear model ARIMA (Auto-Regressive Integrated Moving Average), as nonlinear model LSTM (Long Short-Term Memory) and a HYBRID (LSTM and ARIMA) model built to improve system performance compared to a single model was used. As a result of the study, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values obtained from each of the methods used in the application were compared, and a monthly sales volume budget was created for 2017 with all the methods used. When the MAPE and MSE values obtained from each of these methods were compared, the best performance was the Hybrid model that gave the lowest error, and in addition, the fact that all of the application models got very realistic results by using the historical data showed the success of the predictions..

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Section: The Data Set and Methods Of The Studymentioning

confidence: 99%

Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Temür¹,

Yıldız²

2021

ibr

View full text Add to dashboard Cite

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“…Along with these challenges, new building stocks in developing regions will simultaneously provide opportunities for energy-efficient construction, which could substantially reduce the global energy demand. In developed regions, opportunities to reduce the energy demand will predominantly involve renovating the existing building stock (Prieto et al, 2019a;Chatterjee & Ürge-Vorsatz, 2020).…”

Section: Buildingsmentioning

confidence: 99%

Decarbonisation Pathways for Buildings

Chatterjee¹,

Kiss²,

Ürge-Vorsatz³

et al. 2022

Achieving the Paris Climate Agreement Goals

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This section documents the development of four different energy demand pathways on the basis of the high-efficiency buildings (HEB) model of the Central European University. The assumptions and the scenario narratives are derived and the results provided in numerous graphs and tables. Of the four derived scenarios, two are selected for the OECM and the selection criteria are justified. The results in terms of the global energy demand and energy-related CO2 emissions are provided in tables.

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“…Modelling the energy demand for buildings is a complex task because the building sector-related energy demand depends on several factors, such as spatial resolution, temporal resolution, building physics, and the different technologies of building construction (Prieto et al, 2019;Chatterjee & Ürge-Vorstaz, 2020). The majority of demand models do not incorporate these factors and therefore provide insights into the future energy demand scenarios of the building sector that can be far from realistic (Prieto et al, 2019;Chatterjee & Ürge-Vorstaz, 2020). Therefore, in this study, we use the HEB model to understand the future energy demand potentials for building in key regions across the globe.…”

Section: The High-efficiency Building (Heb) Modelmentioning

confidence: 99%

Methodology

Teske

Orbe

Assaf

et al. 2022

Achieving the Paris Climate Agreement Goals

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The OneEarth Climate Model (OECM), its background, and program architecture are described. How the OECM is broken down into two independent modules to calculate demand and supply is explored. The basic program logic of the MATLAB-based bottom-up demand module, with high technical resolution, is described for various sectors, including the input and output parameters. The description includes numerous figures and tables for both demand and supply modules. The sub-sectors used for the OECM 1.5 °C pathway are listed, including outputs and the areas of use.The second part of the chapter documents the high-efficiency building (HEB) model of the Central European University, which was used for the global and regional bottom-up analyses of the building sector. Its methodology, including the programme architecture, the workflow, and the equations used, is provided.

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Applying modeling and optimization tools to existing city quarters

Cited by 5 publications

References 36 publications

Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

Decarbonisation Pathways for Buildings

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