Skip to main content
SpringerLink
Log in

Query-adaptive training data recommendation for cross-building predictive modeling

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Seyedzadeh S, Rahimian FP, Glesk I, Roper M (2018) Machine learning for estimation of building energy consumption and performance: a review. Vis Eng 6(1):5

    Article  Google Scholar 

  2. Bourdeau M, Qiang Zhai X, Nefzaoui E, Guo X, and Chatellier P, (2019) Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533

  3. Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74:902–924

    Article  Google Scholar 

  4. Apanaviciene R, Vanagas A, Fokaides PA (2020) Smart building integration into a smart city (SBISC): Development of a new evaluation framework. Energies 13(9):2190

    Article  Google Scholar 

  5. Runge J, Zmeureanu R (2019) Forecasting energy use in buildings using artificial neural networks: a review. Energies 12(17):3254

    Article  Google Scholar 

  6. Li C, Ding Z, Zhao D, Yi J, Zhang G (2017) Building energy consumption prediction: an extreme deep learning approach. Energies 10(10):1525

    Article  Google Scholar 

  7. Mocanu E, Nguyen PH, Gibescu M, Kling WL (2016) Deep learning for estimating building energy consumption. Sustain Energy, Grids Netw 6:91–99

    Article  Google Scholar 

  8. Sugiyama M, and Storkey AJ (2007) Mixture regression for covariate shift. In: Advances in neural information processing systems, pp 1337–1344

  9. Zhao S, Li B, Xu P, and Keutzer K (2020) Multi-source domain adaptation in the deep learning era: a systematic survey. arXiv preprint arXiv:2002.12169

  10. Riemer M, Cases I, Ajemian R, Liu M, Rish I, Tu Y, and Tesauro G (2018) Learning to learn without forgetting by maximizing transfer and minimizing interference. arXiv preprint arXiv:1810.11910

  11. Wang Z, Dai Z, Póczos B, and Carbonell J (2019) Characterizing and avoiding negative transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11293–11302

  12. Won C, No S, Alhadidi Q (2019) Factors affecting energy performance of large-scale office buildings: analysis of benchmarking data from New York City and Chicago. Energies 12(24):4783

    Article  Google Scholar 

  13. Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, and Erhan D (2016) Domain separation networks. In: Advances in neural information processing systems, pp 343–351

  14. Rozantsev A, Salzmann M, and Fua P (2018) Beyond sharing weights for deep domain adaptation. IEEE Trans Pattern Anal Mach Intell

  15. Blanchard G, Lee G, and Scott C (2011) Generalizing from several related classification tasks to a new unlabeled sample. In: Advances in neural information processing systems, pp 2178–2186

  16. Muandet K, Balduzzi D, and Schölkopf B (2013) Domain generalization via invariant feature representation. In: International conference on machine learning, pp 10–18

  17. Duan L, Xu D, and Chang S-F (2012) Exploiting web images for event recognition in consumer videos: a multiple source domain adaptation approach. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 1338–1345

  18. Chattopadhyay R, Sun Q, Fan W, Davidson I, Panchanathan S, Ye J (2012) Multisource domain adaptation and its application to early detection of fatigue. ACM Trans Knowl Discov Data (TKDD) 6(4):1–26

    Article  Google Scholar 

  19. Ruder S, Ghaffari P, and Breslin JG (2017) Data selection strategies for multi-domain sentiment analysis. arXiv preprint arXiv:1702.02426

  20. Bromley J, Guyon I, LeCun Y, Säckinger E, and Shah R (1994) Signature verification using a “Siamese” time delay neural network. In: Advances in neural information processing systems, pp 737–744

  21. Melekhov I, Kannala J, and Rahtu E (2016) Siamese network features for image matching. In 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 378–383

  22. Pinheiro PO (2018) Unsupervised domain adaptation with similarity learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8004–8013

  23. Mancini M, Bulo SR, Caputo B, and Ricci E (2019) Adagraph: unifying predictive and continuous domain adaptation through graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6568–6577

  24. Labiadh M, Obrecht C, Ferreira da Silva C, Ghodous P (2021) A microservice-based framework for exploring data selection in cross-building knowledge transfer. SOCA 15(2):97–107

    Article  Google Scholar 

  25. Kaytez F, Taplamacioglu MC, Cam E, Hardalac F (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int J Electr Power Energy Syst 67:431–438

    Article  Google Scholar 

  26. Ferlito S, Atrigna M, Graditi G, De Vito S, Salvato M, Buonanno A and Di Francia G (2015) Predictive models for building’s energy consumption: an artificial neural network (ANN) approach. In: 2015 xviii AISEM annual conference. IEEE, pp 1–4

  27. Biswas MR, Robinson MD, Fumo N (2016) Prediction of residential building energy consumption: a neural network approach. Energy 117:84–92

    Article  Google Scholar 

  28. Li Q, Ren P, and Meng Q (2010) Prediction model of annual energy consumption of residential buildings. In: 2010 international conference on advances in energy engineering. IEEE, pp 223–226

  29. Jain RK, Smith KM, Culligan PJ, Taylor JE (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl Energy 123:168–178

    Article  Google Scholar 

  30. Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural comput 8(7):1341–1390

    Article  Google Scholar 

  31. Frederiks ER, Stenner K, Hobman EV (2015) The socio-demographic and psychological predictors of residential energy consumption: a comprehensive review. Energies 8(1):573–609

    Article  Google Scholar 

  32. Chen Y-T (2017) The factors affecting electricity consumption and the consumption characteristics in the residential sector-a case example of Taiwan. Sustainability 9(8):1484

    Article  Google Scholar 

  33. Fuerst F, Kavarnou D, Singh R, Adan H (2020) Determinants of energy consumption and exposure to energy price risk: a UK study. Z Immob 6(1):65–80

    Google Scholar 

  34. Delzendeh E, Wu S, Lee A, Zhou Y (2017) The impact of occupants’ behaviours on building energy analysis: A research review. Renew Sustain Energy Rev 80:1061–1071

    Article  Google Scholar 

  35. Zhao H-X, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16(6):3586–3592

    Article  Google Scholar 

  36. Kong W, Dong ZY, Hill DJ, Luo F, Xu Y (2017) Short-term residential load forecasting based on resident behaviour learning. IEEE Trans Power Syst 33(1):1087–1088

    Article  Google Scholar 

  37. Wang Y, Liu M, Bao Z, Zhang S (2018) Short-term load forecasting with multi-source data using gated recurrent unit neural networks. Energies 11(5):1138

    Article  Google Scholar 

  38. Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid 10(1):841–851

    Article  Google Scholar 

  39. Tian C, Ma J, Zhang C, Zhan P (2018) A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11(12):3493

    Article  Google Scholar 

  40. Kim T-Y, Cho S-B (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81

    Article  Google Scholar 

  41. Ribeiro M, Grolinger K, ElYamany HF, Higashino WA, Capretz MA (2018) Transfer learning with seasonal and trend adjustment for cross-building energy forecasting. Energy Buildings 165:352–363

    Article  Google Scholar 

  42. Fu Q, Liu Q, Gao Z, Wu H, Fu B, and Chen J (2019) A building energy consumption prediction method based on integration of a deep neural network and transfer reinforcement learning. Int J Pattern Recogn Artif Intell

  43. Hooshmand A, and Sharma R (2019) Energy predictive models with limited data using transfer learning. In: Proceedings of the 10th ACM international conference on future energy systems, pp 12–16

  44. Tian Y, Sehovac L, Grolinger K (2019) Similarity-based chained transfer learning for energy forecasting with big data. IEEE Access 7:139895–139908

    Article  Google Scholar 

  45. Fang X, Gong G, Li G, Chun L, Li W, Peng P (2021) A hybrid deep transfer learning strategy for short term cross-building energy prediction. Energy 215:119208

    Article  Google Scholar 

  46. Fan C, Sun Y, Xiao F, Ma J, Lee D, Wang J, Tseng YC (2020) Statistical investigations of transfer learning-based methodology for short-term building energy predictions. Appl Energy 262:114499

    Article  Google Scholar 

  47. Mocanu E, Nguyen PH, Kling WL, Gibescu M (2016) Unsupervised energy prediction in a smart grid context using reinforcement cross-building transfer learning. Energy Buildings 116:646–655

    Article  Google Scholar 

  48. Oreshkin BN, Carpov D, Chapados N, and Bengio Y (2020) Meta-learning framework with applications to zero-shot time-series forecasting. arXiv preprint arXiv:2002.02887

  49. Pan SJ, Yang Q et al (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  50. Li D, Yang Y, Song Y-Z, and Hospedales TM (2018a) Learning to generalize: Meta-learning for domain generalization. In: 32nd AAAI conference on artificial intelligence

  51. Ghifary M, Bastiaan Kleijn W, Zhang M, and Balduzzi D (2015) Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE international conference on computer vision, pp 2551–2559

  52. Li Y, Tian X, Gong M, Liu Y, Liu T, Zhang K, and Tao D (2018b) Deep domain generalization via conditional invariant adversarial networks. In: Proceedings of the European conference on computer vision (ECCV), pp 624–639

  53. Khosla A, Zhou T, Malisiewicz T, Efros AA, and Torralba A (2012) Undoing the damage of dataset bias. In European conference on computer vision. Springer, pp 158–171

  54. Ding Z, Fu Y (2017) Deep domain generalization with structured low-rank constraint. IEEE Trans Image Process 27(1):304–313

    Article  MATH  Google Scholar 

  55. Li D, Yang Y, Song Y-Z, and Hospedales TM (2017b) Deeper, broader and artier domain generalization. In: Proceedings of the IEEE international conference on computer vision, pp 5542–5550

  56. Xu Z, Li W, Niu L, and Xu D (2014) Exploiting low-rank structure from latent domains for domain generalization. In: European conference on computer vision. Springer, pp 628–643

  57. Mancini M, Bulò SR, Caputo B, and Ricci E (2018) Best sources forward: domain generalization through source-specific nets. In 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 1353–1357

  58. Li Y, Yang Y, Zhou W, and Hospedales TM (2019) Feature-critic networks for heterogeneous domain generalization. arXiv preprint arXiv:1901.11448

  59. Dou Q, de Castro DC, Kamnitsas K, and Glocker B (2019) Domain generalization via model-agnostic learning of semantic features. In: Advances in neural information processing systems, pp 6450–6461

  60. Rosenstein MT, Marx Z, Kaelbling LP, and Dietterich TG ( 2005) To transfer or not to transfer. In: NIPS 2005 workshop on transfer learning, vol 898, pp 1–4

  61. Chen Q, Liu Y, Wang Z, Wassell I, and Chetty K (2018) Re-weighted adversarial adaptation network for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7976–7985

  62. Bhatt HS, Rajkumar A, and Roy, S (2016) Multi-source iterative adaptation for cross-domain classification. In: IJCAI, pp 3691–3697

  63. Borgwardt KM, Gretton A, Rasch MJ, Kriegel H-P, Schölkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):e49–e57

    Article  Google Scholar 

  64. Sugiyama M, Nakajima S, Kashima H, Buenau PV, and Kawanabe M (2008) Direct importance estimation with model selection and its application to covariate shift adaptation. In: Advances in neural information processing systems, pp 1433–1440

  65. Nowozin S, Cseke B, and Tomioka R (2016) f-GAN: Training generative neural samplers using variational divergence minimization. In: Advances in neural information processing systems, pp 271–279

  66. Shen J, Qu Y, Zhang W, and Yu Y (2018) Wasserstein distance guided representation learning for domain adaptation. In: 32nd AAAI conference on artificial intelligence

  67. Kouw WM, and Loog M (2019) A review of domain adaptation without target labels. IEEE Trans Pattern Anal Mach Intell

  68. Shu Y, Cao Z, Long M, Wang J (2019) Transferable curriculum for weakly-supervised domain adaptation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 4951–4958

  69. Fan Y, Tian F, Qin T, Bian J, and Liu T-Y (2017) Learning what data to learn. arXiv preprint arXiv:1702.08635

  70. Yang Y, and Hospedales TM (2014) A unified perspective on multi-domain and multi-task learning. arXiv preprint arXiv:1412.7489

  71. Yang Y, and Hospedales TM (2016) Multivariate regression on the Grassmannian for predicting novel domains. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pap 5071–5080

  72. Hadsell R, Chopra S, and LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 2. IEEE, pp 1735–1742

  73. Iglesias F, Kastner W (2013) Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6(2):579–597

    Article  Google Scholar 

  74. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  MATH  Google Scholar 

  75. Keogh E, Ratanamahatana CA (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358–386

    Article  Google Scholar 

  76. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. Proc VLDB Endow 1(2):1542–1552

    Article  Google Scholar 

  77. Ma Z, and Yan L (2019) Emerging technologies and applications in data processing and management. IGI Global

  78. Salvador S, Chan P (2007) Toward accurate dynamic time warping in linear time and space. Intell Data Anal 11(5):561–580

    Article  Google Scholar 

  79. Lim B, and Zohren S (2020) Time series forecasting with deep learning: a survey. arXiv preprint arXiv:2004.13408

  80. Liu Y, Roberts MC, Sioshansi R (2018) A vector autoregression weather model for electricity supply and demand modeling. J Mod Power Syst Clean Energy 6(4):763–776

    Article  Google Scholar 

  81. Newsham GR, and Birt BJ (2010) Building-level occupancy data to improve arima-based electricity use forecasts. In: Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building, pp 13–18

  82. Goodfellow I, Bengio Y, and Courville A (2016) Deep learning. MIT Press

  83. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  84. Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963

    Article  MATH  Google Scholar 

  85. Zheng J, Xu C, Zhang Z, and Li X (2017) Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: 2017 51st annual conference on information sciences and systems (CISS). IEEE, pp 1–6

  86. Bui V, Kim J, Jang YM et al (2020) Power demand forecasting using long short-term memory neural network based smart grid. In: 2020 international conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 388–391

  87. Lipton ZC (2015) A critical review of recurrent neural networks for sequence learning. CoRR. arXiv:1506.00019

  88. Pascanu R, Mikolov T, and Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning, pp 1310–1318

  89. Gers FA, Schmidhuber J, and Cummins F (1999) Learning to forget: continual prediction with LSTM

  90. Dietterich TG et al (2002) Ensemble learning. Handb Brain Theory Neural Netw 2:110–125

    Google Scholar 

  91. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  92. Ting KM, and Witten IH (1997) Stacked generalization: When does it work?

  93. Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang YJ, Pedersen CO, Strand RK, Liesen RJ, Fisher DE, Witte MJ et al (2001) Energyplus: creating a new-generation building energy simulation program. Energy Buildings 33(4):319–331

    Article  Google Scholar 

  94. Amasyali K, El-Gohary NM (2018) A review of data driven building energy consumption prediction studies. Renew Sustain Energy Rev 81:1192–1205

    Article  Google Scholar 

  95. Kingma DP, and Ba Adam J (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980

Download references

Author information

Authors and Affiliations

  1. LIRIS UMR5205, Univ Lyon, CNRS, Université Claude Bernard, Lyon 1, 69100, Villeurbanne, France

    Mouna Labiadh, Parisa Ghodous & Khalid Benabdeslem

  2. CETHIL UMR5008, Univ Lyon, CNRS, INSA-Lyon, 69621, Villeurbanne, France

    Mouna Labiadh & Christian Obrecht

  3. Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal

    Catarina Ferreira da Silva

Authors
  1. Mouna Labiadh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Obrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Catarina Ferreira da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Parisa Ghodous
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Khalid Benabdeslem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouna Labiadh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Labiadh, M., Obrecht, C., Ferreira da Silva, C. et al. Query-adaptive training data recommendation for cross-building predictive modeling. Knowl Inf Syst 65, 707–732 (2023). https://doi.org/10.1007/s10115-022-01771-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01771-9

Keywords

Search

Navigation