Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Kadric, Dzana; Aganović, Amar; Kadrić, Edin; Delalić-Gurda, Berina; Jackson, Steven

doi:10.1016/j.jobe.2022.105307

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…In addition, many other intelligent algorithms have also been tested for their performance in the economic field, such as long-term memory networks (LSTM), hybrid models, etc. [ [30] , [31] , [32] , [33] ]. But the performance and universality are still under improvement.…”

Section: Introductionmentioning

confidence: 99%

DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Liu,

Ruan,

2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Liu,

Ruan,

2023

Heliyon

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Deep learning–based urban energy forecasting model for residential building energy efficiency

Rani,

Dahiya,

Kundu

et al. 2024

Energy Exploration & Exploitation

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Sustainable and inventive city design is becoming more and more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures is an essential part of the puzzle as it helps conserve resources and keeps the planet habitable. An enhanced Deep Neural Network (DNN) model for household energy efficiency predictions is presented in this research. Our model uses a large dataset of building features, weather, occupancy patterns and energy usage histories. Data is preprocessed, features are engineered and hyperparameters are tweaked to improve DNN prediction. Scalable, easy-to-understand models are essential, as are shifting urban areas and energy landscapes. In this work, the authors have evaluated the proposed model with basic model with different optimizers. Initially, the Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE and 0.0165 MAE. The proposed model gained 99.52% Recall, 98.91% Precision, 99.09% F1-Score, 0.0140 MSE, 0.0137 RMSE and 0.0139 MAE. By monitoring, analyzing and making decisions in real time, smart city systems can help planners understand energy usage trends. The optimized DNN model advances smart city development by promoting sustainability and resource optimization. Predicting residential buildings’ energy efficiency provides proactive energy savings, cost reduction and environmental impact mitigation. The suggested DNN model shows how smart cities use cutting-edge urban planning to become more sustainable, efficient and resilient.

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Estrategias de predicción de consumo energético en edificaciones: una revisión

Ortega-Diaz,

Cárdenas-Rangel,

Osma-Pinto

2023

TecnoL.

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Los edificios son uno de los principales actores contaminantes del medio ambiente, por lo que es necesario fortalecer las estrategias para la reducción de su consumo energético, como el diseño energéticamente eficiente (edificios nuevos) y la gestión energética (edificios existentes). Para ello, es fundamental la predicción del consumo energético que permita conocer el estado de operación de la edificación e inferir sobre las causas de éste y la eficacia de las estrategias de ahorro energético. No obstante, la diversidad de técnicas de predicción del consumo energético existentes dificulta a investigadores su identificación, selección y aplicación. Por ello, a partir de una revisión de la literatura, este artículo identifica técnicas de predicción, expone sus principios teóricos, describe las etapas generales de construcción de un modelo de predicción, reconoce métricas de evaluación, identifica algunas de sus fortalezas y debilidades y presenta criterios para facilitar la selección de una técnica de predicción y métricas de evaluación según las características del caso de estudio. Se realizó un análisis bibliométrico como metodología para identificar y estudiar los artículos más importantes sobre demanda de energía en edificios. Se encuentra que hay tendencia en la aplicación de técnicas de aprendizaje automático y que los modelos de predicción de consumo energético son mayormente aplicados a edificaciones residenciales, comerciales y educativas.

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Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Cited by 5 publications

References 31 publications

DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Deep learning–based urban energy forecasting model for residential building energy efficiency

Estrategias de predicción de consumo energético en edificaciones: una revisión

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