Sliding window-based LightGBM model for electric load forecasting using anomaly repair (original) (raw)

References

  1. Hodge VJ, O’Keefe S, Weeks M, Moulds A (2014) Wireless sensor networks for condition monitoring in the railway industry: a survey. IEEE Trans Intell Transp Syst 16(3):1088–1106
    Article Google Scholar
  2. Hempstead M, Lyons MJ, Brooks D, Wei GY (2008) Survey of hardware systems for wireless sensor networks. J Low Power Electron 4(1):11–20
    Article Google Scholar
  3. Jagannathan S (2016) Real-time big data analytics architecture for remote sensing application. In: International Conference on Signal Processing, Communication, Power and Embedded System, pp 1912–1916 (2016).
  4. Kanoun O, Trankler HR (2004) Sensor technology advances and future trends. IEEE Trans Instrum Meas 53(6):1497–1501
    Article Google Scholar
  5. Bell WR, Hillmer SC (1983) Modeling time series with calendar variation. J Am Stat Assoc 78(383):526–534
    Article Google Scholar
  6. Fu TC (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181
    Article Google Scholar
  7. Ding D, Cooper RA, Pasquina PF, Fici-Pasquina L (2011) Sensor technology for smart homes. Maturitas 69(2):131–136
    Article Google Scholar
  8. Yu Z, Zheng X, Huang F, Guo W, Sun L, Yu Z (2020) A framework based on sparse representation model for time series prediction in smart city. Front Comp Sci 15(1):1–13
    Google Scholar
  9. Chou JS, Ngo NT (2016) Smart grid data analytics framework for increasing energy savings in residential buildings. Autom Constr 72:247–257
    Article Google Scholar
  10. Tabrizchi H, Javidi MM, Amirzadeh V (2019) Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation. Evolv Syst 1–13 (2019)
  11. Park S, Moon J, Hwang E (2019) 2-Stage electric load forecasting scheme for day-ahead CCHP scheduling. In: IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS), pp 1–4
  12. Montazerolghaem A, Moghaddam MHY, Leon-Garcia A (2017) OpenAMI: Software-defined AMI load balancing. IEEE Internet Things J 5(1):206–218
    Article Google Scholar
  13. Raciti M, Nadjm-Tehrani S (2013) Embedded cyber-physical anomaly detection in smart meters. In: Critical information infrastructures security, pp 34–45
  14. Jiang R, Lu R, Wang Y, Luo J, Shen C, Shen X (2014) Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci Technol 19(2):105–120
    Article Google Scholar
  15. Moghaddass R, Wang J (2017) A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans Smart Grid 9(6):5820–5830
    Article Google Scholar
  16. Zhang W, Yang Q, Geng Y (2009) A survey of anomaly detection methods in networks. In: International symposium on computer network and multimedia technology, pp 1–3
  17. Wang C, Viswanathan K, Choudur L, Talwar V, Satterfield W, Schwan K (2011) Statistical techniques for online anomaly detection in data centers. In: 12th IFIP/IEEE international symposium on integrated network management and workshops, pp 385–392
  18. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):1–58
    Article Google Scholar
  19. Zhang A, Song S, Wang J, Yu PS (2017) Time series data cleaning: from anomaly detection to anomaly repairing. Proc VLDB Endowm 10(10):1046–1057
    Article Google Scholar
  20. Jung S, Moon J, Park S, Rho S, Baik SW, Hwang E (2020) Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation. Sensors 20(6):1772
    Article Google Scholar
  21. Armstrong JS (1989) Combining forecasts: the end of the beginning or the beginning of the end? Int J Forecast 5:585–588
    Article Google Scholar
  22. Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp 93–104
  23. Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: 8th IEEE International Conference on Data Mining, pp 413–422
  24. Chen J, Sathe S, Aggarwal C, Turaga D (2017) Outlier detection with autoencoder ensembles. In: Proceedings of the SIAM International Conference on Data Mining, pp 90–98
  25. Akouemo HN, Povinelli RJ (2017) Data improving in time series using ARX and ANN models. IEEE Trans Power Syst 32(5):3352–3359
    Article Google Scholar
  26. Araya DB, Grolinger K, ElYamany HF, Capretz MA, Bitsuamlak G (2017) An ensemble learning framework for anomaly detection in building energy consumption. Energy Build 144:191–206
    Article Google Scholar
  27. Xu CD, Wang JF, Hu MG, Li QX (2013) Interpolation of missing temperature data at meteorological stations using P-BSHADE. J Clim 26(19):7452–7463
    Article Google Scholar
  28. Habermann C, Kindermann F (2007) Multidimensional spline interpolation: theory and applications. Comput Econ 30(2):153–169
    Article Google Scholar
  29. Gan S, Wang S, Chen Y, Zhang Y, Jin Z (2015) Dealiased seismic data interpolation using seislet transform with low-frequency constraint. IEEE Geosci Remote Sens Lett 12(10):2150–2154
    Article Google Scholar
  30. Jurado S, Peralta J, Nebot A, Mugica F, Cortez P (2013) Short-term electric load forecasting using computational intelligence methods. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–8
  31. Grolinger K, L’Heureux A, Capretz MA, Seewald L (2016) Energy forecasting for event venues: Big data and prediction accuracy. Energy Build 112:222–233
    Article Google Scholar
  32. Abbasi RA, Javaid N, Ghuman MNJ, Khan ZA, Rehman SU (2019) Short term load forecasting using XGBoost. In: Workshops of the International Conference on Advanced Information Networking and Applications, pp 1120–1131
  33. Kuo PH, Huang CJ (2018) A high precision artificial neural networks model for short-term energy load forecasting. Energies 11(1):213
    Article Google Scholar
  34. Massana J, Pous C, Burgas L, Melendez J, Colomer J (2015) Short-term load forecasting in a non-residential building contrasting models and attributes. Energy Build 92:322–330
    Article Google Scholar
  35. Park S, Moon J, Jung S, Rho S, Baik SW, Hwang E (2020) A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling. Energies 13(2):443
    Article Google Scholar
  36. Wang P, Liu B, Hong T (2016) Electric load forecasting with recency effect: a big data approach. Int J Forecast 32:585–597
    Article Google Scholar
  37. Xie J, Chen Y, Hong T, Laing TD (2016) Relative humidity for load forecasting models. IEEE Trans Smart Grid 9:191–198
    Article Google Scholar
  38. Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233–243
    Article Google Scholar
  39. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint http://arxiv.org/abs/1312.6114
  40. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: a highly efficient gradient boosting decision tree. In: Advances in neural information processing systems, pp 3146–3154
  41. Moon J, Jung S, Rew J, Rho S, Hwang E (2020) Combination of short term load forecasting models based on a stacking ensemble approach. Energy Build 109921

Download references