Machine learning based tool for identifying errors in CAD to GIS converted data

Badhrudeen, Mohamed; Naranjo, Nalin; Movahedi, Ali; Derrible, Sybil

doi:10.1007/978-981-15-0802-8_190

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Recently, machine learning (ML) approaches have gained significant interest (Bourdeau et al, 2019;Seyedzadeh et al, 2019;Yu et al, 2016;Froemelt et al, 2019) and tend to perform better than traditional statistical regression modeling techniques thanks to their machine-based algorithmic computation. In particular, Decision Tree (DT) (Badhrudeen et al, 2020), ANN, ensemble methods (e.g., bagging and boosting methods), Support Vector Machine (SVM) (Parsa et al, 2019b), and regularized linear regression (e.g., ridge and lasso regression) tend to be the most popular ML approaches applied in the previous studies (Wei et al, 2018). For instance, Ahmad et al (2014) forecasted building electricity consumption by using ANN and SVM, and Li et al (2015) predicted building electricity consumption using ANNs and principle component analysis (PCA).…”

Section: Literature Reviewmentioning

confidence: 99%

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Movahedi¹,

Derrible²

2020

Preprint

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As cities keep growing worldwide, so does the demand for key resources such as energy (electricity and gas) and water that residents consume. Meeting the demand for these resources can be challenging and requires an understanding of their consumptions patterns. In this work, we apply XGBoost (Extreme Gradient Boosting) to predict and analyze water and energy consumption in large-scale buildings in New York City. For this, the New York City’s local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output (PLUTO) dataset as well as with other socio-economic databases. Specifically, we developed three models: electricity, gas, and water consumption. Seven major lessons were learnt in terms of interrelationships between electricity, gas, and water consumption. In particular, water and gas consumption are highly interrelated with one another (often because gas is used for water heating). Furthermore, electricity consumption is affected by building type, and electricity and water consumption are particularly interrelated in nonresidential buildings. Overall, the knowledge gained from the models and from the SHAP analysis can help planners, engineers, and policymakers develop more effective strategies and help them manage the demand for energy and water in large-scale buildings.

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Section: Literature Reviewmentioning

confidence: 99%

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Movahedi¹,

Derrible²

2020

Preprint

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Suitable sites identification for potential rainwater harvesting (PRWH) using a multi-criteria decision support system (MCDSS)

Radwan

Alazba

2022

Acta Geophys.

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Interrelationships between urban travel demand and electricity consumption: a deep learning approach

Movahedi

Parsa

Rozhkov

et al. 2023

Sci Rep

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The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago.

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Machine learning based tool for identifying errors in CAD to GIS converted data

Cited by 5 publications

References 12 publications

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Suitable sites identification for potential rainwater harvesting (PRWH) using a multi-criteria decision support system (MCDSS)

Interrelationships between urban travel demand and electricity consumption: a deep learning approach

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