Implementation of energy performance assessment system for existing building

Jeong, Youn-Kwae; Kim, Tae-Hyung; Nam, Hong-Soon; Lee, Il-Woo

doi:10.1109/ictc.2016.7763504

Cited by 8 publications

(11 citation statements)

References 2 publications

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“…Capitalises on the merits of individual methods Gradient boosting machines, Random forest [45], [75], [85] [86], [116], [187], [195] Clustering algorithms: k-Means [41], [50], [69], [71], [207], k-Medians, Expectation maximization [50], [73], Hierarchical clustering [41], [50], [207] Useful for making sense of data  Results are sometimes difficult to interpret  Very limited when dealing with unfamiliar datasets Dimensionality reduction algorithms: Principal component analysis [41], [64], [73], [79], [83], [131], [135], [165], Principal component regression [41], Partial least squares regression [21] Good for handling large datasets without necessarily making assumptions on data  Not effective when dealing with non-linear data  It is sometimes difficult to understand the meaning of the results…”

Section:  Overfitting Problems  May Also Incorporate the Weaknesses Of Individual Methods If Not Adequately Processedmentioning

confidence: 99%

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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Section:  Overfitting Problems  May Also Incorporate the Weaknesses Of Individual Methods If Not Adequately Processedmentioning

confidence: 99%

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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“…For our analysis we use excerpts of MIDI or MIDI-like recordings with performed notes matched to their corresponding score notes extracted from two datasets: Vienna 4x22: This dataset was originally compiled by Goebl [12] and consists of 4 excerpts of solo piano pieces, each performed by KAIST / International Piano-e-Competition: This dataset consists of MIDI recordings of performances of several editions of the International Piano-e-Competition 1 for a number of which researchers at KAIST [17] collected and corrected scores in MusicXML format. All performances were recorded on Yamaha Disklavier instruments.…”

Section: Datasetsmentioning

confidence: 99%

“…The recent years have seen the creation and publication of several corpora of precisely measured and score-aligned piano performances within MIR and digital musicology communities [17,23,29]. This renewed interest in computational models of expressive piano performance, in particular the data-driven kind.…”

Section: Introductionmentioning

confidence: 99%

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Sounding Out Reconstruction Error-Based Evaluation of Generative Models of Expressive Performance

Peter,

Cancino-Chacón,

Karystinaios

et al. 2023

Proceedings of the 10th International Conference on Digital Libraries for Musicology

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Generative models of expressive piano performance are usually assessed by comparing their predictions to a reference human performance. A generative algorithm is taken to be better than competing ones if it produces performances that are closer to a human reference performance. However, expert human performers can (and do) interpret music in different ways, making for different possible references, and quantitative closeness is not necessarily aligned with perceptual similarity, raising concerns about the validity of this evaluation approach. In this work, we present a number of experiments that shed light on this problem. Using precisely measured high-quality performances of classical piano music, we carry out a listening test indicating that listeners can sometimes perceive subtle performance difference that go unnoticed under quantitative evaluation. We further present tests that indicate that such evaluation frameworks show a lot of variability in reliability and validity across different reference performances and pieces. We discuss these results and their implications for quantitative evaluation, and hope to foster a critical appreciation of the uncertainties involved in quantitative assessments of such performances within the wider music information retrieval (MIR) community. CCS CONCEPTS• Applied computing → Sound and music computing; Performing arts; • Information systems → Evaluation of retrieval results.

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“…Recently there has been increasing interest in developing an approach to estimating the energy performance of residential buildings (Tsanas and Xifara, 2012;Jeon et al 2016). Many techniques have been proposed for energy performance in buildings.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Regression Learning Methods for Estimation of Energy Performance of Residential Structures

Akgündoğdu

2020

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Energy efficiency is a top priority for private and commercial buildings. This study evaluates the performance of six regression learning methods, including Linear Regressor, MLP Regressor, RBF Regressor, SVM Regressor, Gaussian Processes, and ANFIS Regressor to predict the heating and cooling loads of residential buildings. 768 buildings were considered and analyzed based on the influential parameters, such as relative density, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution for predicting heating load and cooling load. Three statistical criteria such as correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used to assess the potential of the regression methods used in this study. The best estimation results were obtained with the ANFIS regression model, with

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Implementation of energy performance assessment system for existing building

Cited by 8 publications

References 2 publications

Towards developing a systematic knowledge trend for building energy consumption prediction

Towards developing a systematic knowledge trend for building energy consumption prediction

Sounding Out Reconstruction Error-Based Evaluation of Generative Models of Expressive Performance

Comparative Analysis of Regression Learning Methods for Estimation of Energy Performance of Residential Structures

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