Deep Feature Selection for Wind Forecasting‐II

Manoj, Sumitha; Ananth, J. P.; Dhanka, Balan; Kamatchi, Maharaja

doi:10.1002/9781119761686.ch8

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…54 MSE and RMSE cannot be directly compared to assess the suitability of the model. 52,54 Whenever a significant difference is present, it may be caused by errors due to outliers that skew the datasets. Therefore, several dataset error analyses are required to assess the model’s performance alongside the coefficient of determination (R2).…”

Section: Methodsmentioning

confidence: 99%

“…53 Hence, the MSE values are often modified as Root Mean Square Error (RMSE), by taking the square root of MSE, resulting in a smaller difference between the two types of error. 54 MSE and RMSE cannot be directly compared to assess the suitability of the model. 52,54 Whenever a significant difference is present, it may be caused by errors due to outliers that skew the datasets.…”

Section: Machine Learning Modelingmentioning

confidence: 99%

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An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Faridah,

Utami,

Wijaya

et al. 2024

Building Services Engineering Research and Technology

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Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of [Formula: see text] and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study.

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