Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2021

16.10.2020 | Original Article

A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression

verfasst von: Paulino José García-Nieto, Esperanza García-Gonzalo, José Pablo Paredes-Sánchez, Antonio Bernardo Sánchez

Erschienen in: Neural Computing and Applications | Ausgabe 12/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

An energy performance certificate (EPC) provides information on the energy performance of an energy system. The objective of this research aimed at obtaining a predictive model for early detection of thermal power efficiency (TPE) for energy conversion and preservation in buildings. This article expounds a sound and solid nonparametric Bayesian technique known as Gaussian process regression (GPR) approach, based on a set of data collected from different dwellings in an oceanic climate. Firstly, this model introduces the relevance of each predictive variable on energy performance in residential buildings. The second result refers to the statement that we can predict successfully the TPE by using this model. A coefficient of determination equal to 0.9687 was thus established in order to predict the TPE from the observed data, using the GPR approach in combination with the differential evolution (DE) optimiser. The concordance between experimental observed data and the predicted data from the best-proposed novel hybrid DE/GPR-relied model demonstrated here the adequate efficiency of this innovative approach.
Vorheriger Artikel Multiple delay-dependent finite-time boundedness and input–output finite-time mean square stabilization of uncertain semi-Markovian jump systems with input constraint
Nächster Artikel Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Bourdeau M, Zhai XQ, Nefzaoui E, Guo X, Chatellier P (2019) Modelling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533
2.
Harish VSKV, Kumar A (2016) A review on modeling and simulation of building energy systems. Renew Sust Energ Rev 56:1272–1292
3.
European Commission (2002) Directive 2002/91/EC of the European parliament and of the council of 16 December 2002 on the energy performance of buildings, Official Journal of the European Communities
4.
Paredes-Sánchez BM, Paredes-Sánchez JP, García Nieto PJ (2020) Energy multiphase model for biocoal conversion systems by means of a nodal network. Energies 13:2728–2740
5.
Paredes-Sánchez JP, Conde M, Gómez MA, Alves D (2018) Modelling hybrid thermal systems for district heating: a pilot project in wood transformation industry. J Clean Prod 194:726–734
6.
Li Y, Kubicki S, Guerriero A, Rezgui Y (2019) Review of building energy performance certification schemes towards future improvement. Renew Sust Energ Rev 113:109244
7.
Khayatian F, Sarto L, Dall’O’ G (2016) Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build 125:45–54
8.
Son H, Kim C (2015) Early prediction of the performance of green building projects using pre-project planning variables: data mining approaches. J Clean Prod 109:144–151
9.
Melo AP, Versage RS, Sawaya G, Lamberts R (2016) A novel surrogate model to support building energy labelling system: a new approach to assess cooling energy demands in commercial buildings. Energy Build 131:233–247
10.
Hensen JLM, Lamberts R (2019) Building performance simulation for design and operation. Routledge, New York
11.
de Wilde P (2018) Building performance analysis. Wiley-Blackwell, New York
12.
Rasmussen CE (2003) Gaussian processes in machine learning: summer school on machine learning. Springer, Berlin
13.
14.
Dym H, McKean HP (2008) Gaussian processes, function theory, and the inverse spectral problem. Dover, New YorkMATH
15.
Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetMATH
16.
Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, BerlinMATH
17.
Feoktistov V (2006) Differential evolution: in search of solutions. Springer, New YorkMATH
18.
Rocca P, Oliveri G, Massa A (2011) Differential evolution as applied to electromagnetics. IEEE Antennas Propag 53(1):38–49
19.
Man K-F, Tang K-S, Kwong S (1999) Genetic algorithms: concepts and designs. Springer, New YorkMATH
20.
Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley-Interscience, New YorkMATH
21.
Goldberg DE (2008) Genetic algorithms in search, optimization and machine learning. Dorling Kindersley Pvt Ltd, London
22.
Eiben AE, Smith JE (2015) Introduction to evolutionary computing. Springer, New YorkMATH
23.
Kramer O (2017) Genetic algorithm essentials. Springer, BerlinMATH
24.
Matthies H, Strang G (1979) The solution of nonlinear finite element equations. Int J Numer Meth Eng 14(11):1613–1626MathSciNetMATH
25.
Nocedal J (1980) Updating quasi-Newton matrices with limited storage. Math Comput 35(151):773–782MathSciNetMATH
26.
Liu DC, Nocedal J (1989) On the limited memory BFGS method for large scale optimization. Math Program 45:503–528MathSciNetMATH
27.
Byrd RH, Lu P, Nocedal J, Zhu C (1994) A limited-memory algorithm for bound constrained optimization. SIAM J Sci Comp 16:1190–1208MathSciNetMATH
28.
Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-BFGS–B: Fortran subroutines for large-scale bound-constrained optimization. ACM T Math Software 23(4):550–560MathSciNetMATH
29.
Rao SS (2009) Engineering optimization: theory and practice. Wiley, New York
30.
Nesterov Y (2018) Lectures on convex optimization. Springer, BerlinMATH
31.
Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. The MIT Press, CambridgeMATH
32.
Duan Y, Cooling C, Ahn JS, Jackson C, Flint A, Eaton MD, Bluck MJ (2019) Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations. Int J Heat Fluid Fl 80:108497
33.
Wang S, Zhu L, Fuh JYH, Zhang H, Yan W (2020) Multi-physics modeling and Gaussian process regression analysis of cladding track geometry for direct energy deposition. Opt Laser Eng 127:105950
34.
Akhlaghi YG, Zhao X, Shittu S, Badiei A, Cattaneo MEGV, Ma X (2019) Statistical investigation of a dehumidification system performance using Gaussian process regression. Energ Buildings 202:109406
35.
Alghamdi AS, Polat K, Alghoson A, Alshdadi AA, Abd El-Latif AA (2020) Gaussian process regression (GPR) based non-invasive continuous blood pressure prediction method from cuff oscillometric signals. Appl Acoust 164:107256
36.
Li X, Yuan C, Li X, Wang Z (2020) State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression. Energy 190:116467
37.
Zeng A, Ho H, Yu Y (2020) Prediction of building electricity usage using Gaussian Process Regression. J Build Eng 28:101054
38.
Ambrogioni L, Maris E (2019) Complex-valued Gaussian process regression for time series analysis. Signal Process 160:215–228
39.
Cai H, Jia X, Feng J, Li W, Hsu Y, Lee J (2020) Gaussian Process Regression for numerical wind speed prediction enhancement. Renew Energ 146:2112–2123
40.
Gao A, Liao W (2019) Efficient gravity field modeling method for small bodies based on Gaussian process regression. Acta Astronaut 157:73–91
41.
Gonçalves IG, Echer E, Frigo E (2020) Sunspot cycle prediction using warped Gaussian process regression. Adv Space Res 65(1):677–683
42.
Sarkar D, Contal E, Vayatis N, Dias F (2016) Prediction and optimization of wave energy converter arrays using a machine learning approach. Renew Energ 97:504–517
43.
Zhang J, Taflanidis AA, Scruggs JT (2020) Surrogate modeling of hydrodynamic forces between multiple floating bodies through a hierarchical interaction decomposition. J Comput Phys 408:109298MathSciNet
44.
Zhao H, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–3592
45.
Ahmad T, Chen H (2020) A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustain Cities Soc 54:102010
46.
Jovanovic RZ, Sretenovic AA, Zivkovic BD (2015) Ensemble of various neural networks for prediction of heating energy consumption. Energy Build 94:189–199
47.
Rampazzo M, Lionello M, Beghi A, Sisti E, Cecchinato L (2019) A static moving boundary modelling approach for simulation of indirect evaporative free cooling systems. Appl Energ 250:1719–1728
48.
Chou JS, Bui DK (2014) Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Eng Build 82:437–446
49.
Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109
50.
Aydinalp-Koksal M, Ugursal VI (2008) Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl Eng 85:271–296
51.
Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renew Sustain Eng Rev 81:1192–1205
52.
Yoon YR, Moon HJ (2018) Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression. Energy Build 168:215–224
53.
Gray FM, Schmidt M (2016) Thermal building modelling using Gaussian processes. Energy Build 119:119–128
54.
Mustapa RF, Dahlan NY, Yassin AIM, Nordin AHM (2020) Quantification of energy savings from an awareness program using NARX-ANN in an educational building. Energy Build 215:109899
55.
56.
57.
Rychlik I, Johannesson P, Leadbetter MR (1997) Modelling and Statistical Analysis of ocean-wave data using transformed Gaussian processes. Mar Struct 10(1):13–47
58.
Bishop CM (2011) Pattern recognition and machine learning. Springer, New YorkMATH
59.
Li M, Sadoughi M, Hu Z, Hu C (2020) A hybrid Gaussian process model for system reliability analysis. Reliab Eng Syst Safe 197:106816
60.
Daemi A, Kodamana H, Huang B (2019) Gaussian process modelling with Gaussian mixture likelihood. J Process Contr 81:209–220
61.
Ciaburro G (2017) MATLAB for machine learning. Packt Publishing, Birmingham
62.
Lantz B (2019) Machine learning with R: expert techniques for predictive modeling. Packt Publishing, Birmingham
63.
Simon D (2013) Evolutionary optimization algorithms. Wiley, New York
64.
Yang X, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier, London
65.
Liu J, Lampinen J (2002) On setting the control parameter of the differential evolution method. In: Proceedings of the 8th international conference on soft computing, MENDEL, Brno, Czech Republic, pp 11–18
66.
Knafl GJ, Ding K (2016) Adaptive regression for modeling nonlinear relationships. Springer, BerlinMATH
67.
McClave JT, Sincich TT (2016) Statistics. Pearson, New YorkMATH
68.
69.
Stone JV (2016) Bayes’ rule with python: a tutorial introduction to Bayesian analysis. Sebtel Press, London
70.
Seeger M (2000) Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers. In: NIPS’99 Proceedings of the 12th International Conference on Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, vol. 12, pp 603–609
71.
Piironen J, Vehtari A (2016) Projection predictive model selection for Gaussian processes. IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE Publisher, Vietri sul Mare, pp 1–6
72.
Paananen T, Piironen J, Andersen MR, Vehtari A (2019) Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research (PMLR), Naha, Okinawa, Japan, pp 1743–1752
73.
Ye H, Ren Q, Hu X, Lin T, Shi L, Zhang G, Li X (2018) Modeling energy-related CO2 emissions from office buildings using general regression neural network. Resour Conserv Recy 129:168–174
Metadaten
Titel
A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression
verfasst von
Paulino José García-Nieto
Esperanza García-Gonzalo
José Pablo Paredes-Sánchez
Antonio Bernardo Sánchez
Publikationsdatum
16.10.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05427-z

Weitere Artikel der Ausgabe 12/2021

Neural Computing and Applications 12/2021 Zur Ausgabe

Original Article

Online reliability optimization for URLLC in HetNets: a DQN approach

Original Article

Multi-granularity semantic representation model for relation extraction

Original Article

Multiple delay-dependent finite-time boundedness and input–output finite-time mean square stabilization of uncertain semi-Markovian jump systems with input constraint

Original Article

Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Original Article

Neural-assisted image-dependent encryption scheme for medical image cloud storage

Original Article

Optimal power flow solution with stochastic wind power using the Lévy coyote optimization algorithm

Premium Partner

    Bildnachweise