Journal ArticleDOI
Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification
TLDR
This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patterns classification and illustrates that the proposed strategy is reliable and effective and can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.About:
This article is published in Energy and Buildings.The article was published on 2021-06-15. It has received 78 citations till now. The article focuses on the topics: Energy consumption & Ensemble learning.read more
Citations
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Journal ArticleDOI
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
TL;DR: In this paper, the authors presented the utilization of several machine learning techniques such as Artificial Neural Network (ANN), Gradient Boosting (GB), Deep Neural Networks (DNN), Random Forest (RF), Stacking, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision tree (DT) and Linear Regression (LR) for predicting annual building energy consumption using a large dataset of residential buildings.
Journal ArticleDOI
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
TL;DR: In this article , the authors presented the utilization of several machine learning techniques such as Artificial Neural Network (ANN), Gradient Boosting (GB), Deep Neural Networks (DNN), Random Forest (RF), Stacking, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision tree (DT) and Linear Regression (LR) for predicting annual building energy consumption using a large dataset of residential buildings.
Journal ArticleDOI
Machine learning for energy performance prediction at the design stage of buildings
TL;DR: It is shown that it is possible to develop a high performing ML model for building energy use prediction at the design stage and Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance.
Journal ArticleDOI
Problem of data imbalance in building energy load prediction: Concept, influence, and solution
TL;DR: A clustering decision tree-based multi-model prediction method is proposed to solve the data imbalance problem in building energy load prediction and shows that the proposed method has better prediction performance than the conventional single model-based method.
Journal ArticleDOI
Comparison of machine-learning models for predicting short-term building heating load using operational parameters
TL;DR: In terms of prediction accuracy and model stability, the Gaussian process regression model had the best overall performance among the 15 models, while the support vector machine had the faster calculation speed with acceptable prediction accuracy.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book ChapterDOI
Ensemble Methods in Machine Learning
TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Journal ArticleDOI
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more
TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
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