A Review of the Main Machine Learning Methods for Predicting Residential Energy Consumption

González‐Briones, Alfonso; Hernández, Guillermo; Pinto, Tiago; Vale, Zita; Corchado, Juan M.

doi:10.1109/eem.2019.8916406

Cited by 19 publications

(4 citation statements)

References 23 publications

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“…C) Direct down. The areas to which these techniques can be applied are diverse, ranging from energy optimization and industry 4.0 processes to education and medical care [9], [6], [22], [10]. Data analysis in the dental sector has been a common practice for decades and it has been used to treat different diseases, and dental and mouth-related problems [28], as well as other general problems [19].…”

Section: Biological Backgroundmentioning

confidence: 99%

A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents

Hernández,

González-Briones,

Machado

et al. 2023

Communications in Computer and Information Science

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There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making.. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as "limited cases", in which an expert's opinion is necessary.

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Section: Biological Backgroundmentioning

confidence: 99%

A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents

Hernández,

González-Briones,

Machado

et al. 2023

Communications in Computer and Information Science

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“…SGD regression selects only one random training sample at a time to evaluate gradients. Then it calculates the gradient for the cluster it chooses [17]. With this technique, it works much faster than the gradient descent algorithm in large data sets [18].…”

Section: Stochastic Gradient Descent (Sgd) Regressionmentioning

confidence: 99%

“…In the XGBoost algorithm, the information gain of the objective function after each split is obtained as in Equation (17).…”

Section: Extreme Gradient Boosting Regression (Xgboost)mentioning

confidence: 99%

Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data

Apaydın¹,

Yumuş²,

Değırmencı³

et al. 2022

Pamukkale J Eng Sci

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Weather has a significant impact on human life and activities. As abrupt changes in air temperature negatively affect daily life and various industries, the importance of weather forecast accuracy is increasing day by day. Current weather forecasting methods can be divided into two main groups: numerical-based and machine learning-based approaches. Numerical-based weather forecasting methods use complex mathematical formulas that significantly increase the computational cost. On the other hand, machine learning-based methods have been preferred more in recent years due to their lower computational costs. In this study, the next day's maximum and minimum air temperature are estimated for Seoul, South Korea by using 12 different regression methods together with the boosting-based machine learning algorithms developed in recent years, as well as traditional machine learning methods. Furthermore, since tuning of hyperparameters affects the process time and performance of machine learning algorithms, all 12 methods have been extensively studied in terms of time and hyperparameters. The square correlation coefficient (𝑅 2 ), which is frequently adopted in the literature, is used to compare the performances of the methods. According to the observed results, the boosting-based XGBoost and LightGBM methods are the most successful machine learning algorithms in predicting the maximum and minimum air temperature for all years with both statistical test analysis and the highest 𝑅 2 score. Havanın insan yaşamı ve faaliyetleri üzerinde önemli bir etkisi vardır. Hava sıcaklığındaki ani değişimler günlük yaşamı ve çeşitli endüstrileri olumsuz etkilendiğinden hava tahmini doğruluğunun önemini günden güne artırmaktadır. Mevcut hava tahmin yöntemleri iki ana gruba ayrılabilir: sayısal tabanlı ve makine öğrenim tabanlı yaklaşımlar.Sayısal tabanlı hava tahmin yöntemleri, hesaplama maliyetini önemli ölçüde artıran karmaşık matematiksel formüller kullanır. Buna karşın, makine öğrenim tabanlı yöntemler ise düşük işlem maliyetleri nedeniyle son yıllarda daha çok tercih edilir. Bu çalışmada, geleneksel makine öğrenmesi yöntemlerinin yanı sıra son yıllarda geliştirilen yükseltme tabanlı makine öğrenmesi algoritmaları ile birlikte 12 farklı regresyon yöntemi kullanılarak Güney Kore Seul için bir sonraki günün maksimum ve minimum hava sıcaklığı tahmin edilmektedir. Ayrıca, hiperparametrelerin ayarlanması, makine öğrenmesi algoritmalarının işlem süresini ve performansını etkilediğinden, 12 yöntemin tümü zaman ve hiperparametreler açısından kapsamlı bir şekilde çalışılmııştır. Yöntemlerin performanslarının karşılaştırılmasında literatürde sıklıkla tercih edilen kare korelasyon katsayısı (𝑅 2 ) kullanılmaktadır. Gözlemlenen sonuçlara göre, yükseltme tabanlı XGBoost ve LightGBM yöntemleri, hem istatistiksel test analizi hem de en yüksek 𝑅 2 puanı ile tüm yıllar için maksimum ve minimum hava sıcaklığını tahmin etmede en başarılı makine öğrenmesi algoritmalarıdır.

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“…There are a number of techniques for predicting and estimating future values. In the field of energy optimization they have been used to calculate the energy demand of buildings or homes [28]. In this paper, prediction will be used to validate whether the decisions of intelligent temperature adjustment algorithms produce the desired results (reduced energy consumption).…”

Section: Energy Consumption Predictionmentioning

confidence: 99%

Virtual agent organizations for user behaviour pattern extraction in energy optimization processes: A new perspective

et al. 2021

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The optimization of energy use in family homes and public buildings is an ongoing topic of discussion. State-of-the-art research has almost always focused on reducing the consumption of heating systems, air-conditioning or lighting. Despite their importance, user-related variables, such as comfort, are normally not included in the optimization process. These aspects should be considered to be able to e↵ectively minimize energy consumption. Thus, there is a need for a comprehensive energy optimization approach, one that will consider both climatological factors and user behaviour. Learning about user behaviour is key to e↵ective optimization. In this work, the proposed architecture's capacity to organize Virtual Agent Organizations (VAO) allows it to adapt to highly variable user behavior and preferences. This agent methodology has the ability to manage Wireless Sensor Networks (WSNs), Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) to obtain user preferences and predict their behaviour in the home or building. The proposed approach has been tested in two di↵erent buildings, a traditional-construction house and a modular home, obtaining savings of 30.16% and 13.43%, respectively. These results validate the proposed mixed approach of temperature adjustment algorithms together with the extraction of user behavior patterns for the establishment of a threshold based on preferences.

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A Review of the Main Machine Learning Methods for Predicting Residential Energy Consumption

Cited by 19 publications

References 23 publications

A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents

A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents

Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data

Virtual agent organizations for user behaviour pattern extraction in energy optimization processes: A new perspective

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