Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada



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Submitted Apr 17, 2020
Published May 12, 2020
10.24018/ejece.2020.4.3.210

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A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

Keywords: Load forecast Stationary wavelet transform Long short-term memory Neural Network

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References

  1. M. Q. Raza and A. Khosravi, ?A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings,? Renewable and Sustainable Energy Reviews, vol. 50, pp. 1352?1372, 2015.
     Google Scholar
  2. F. Mcloughlin, A. Duffy, and M. Conlon, ?Evaluation of time series techniques to characterise domestic electricity demand,? Energy, vol. 50, pp. 120?130, 2013.
     Google Scholar
  3. M. Aydinalp-Koksal and V. I. Ugursal, ?Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector,? Applied Energy, vol. 85, no. 4, pp. 271?296, 2008.
     Google Scholar
  4. M. Imani and H. Ghassemian, ?Residential load forecasting using wavelet and collaborative representation transforms,? Applied Energy, vol. 253, p. 113505, 2019.
     Google Scholar
  5. K. Yan, W. Li, Z. Ji, M. Qi, and Y. Du, ?A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households,? IEEE Access, vol. 7, pp. 157633?157642, 2019.
     Google Scholar
  6. M. Aydinalp, V. I. Ugursal, and A. S. Fung, ?Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks,? Applied Energy, vol. 79, no. 2, pp. 159?178, 2004.
     Google Scholar
  7. Ringwood JV, Bofell D, Murray FT, ?Forecasting electricity demand on short, medium and long time scales using neural networks,? Journal of Intelligent and Robotic Syst 2001;31:129?47.
     Google Scholar
  8. Z. W. Geem and W. E. Roper, ?Energy demand estimation of South Korea using artificial neural network,? Energy Policy, vol. 37, no. 10, pp. 4049?4054, 2009.
     Google Scholar
  9. A. Baliyan, K. Gaurav, and S. K. Mishra, ?A Review of Short Term Load Forecasting using Artificial Neural Network Models,? Procedia Computer Science, vol. 48, pp. 121?125, 2015.
     Google Scholar
  10. A. Ghanbari, S. Abbasian-Naghneh, and E. Hadavandi, ?An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic,? 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2011.
     Google Scholar
  11. S. Bouktif, A. Fiaz, A. Ouni, and M. Serhani, ?Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches ?,? Energies, vol. 11, no. 7, p. 1636, 2018.
     Google Scholar
  12. N. Amjady and F. Keynia, ?Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm,? Energy, vol. 34, no. 1, pp. 46?57, 2009.
     Google Scholar
  13. J. Zheng, C. Xu, Z. Zhang, and X. Li, ?Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network,? 2017 51st Annual Conference on Information Sciences and Systems (CISS), 2017.
     Google Scholar
  14. A. K. Fard and M.-R. Akbari-Zadeh, ?A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting,? Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, no. 2, pp. 167?182, 2013.
     Google Scholar
  15. J. Kim, J. Moon, E. Hwang, and P. Kang, ?Recurrent inception convolution neural network for multi short-term load forecasting,? Energy and Buildings, vol. 194, pp. 328?341, 2019.
     Google Scholar
  16. Y. Liu, L. Guan, C. Hou, H. Han, Z. Liu, Y. Sun, and M. Zheng, ?Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform,? Applied Sciences, vol. 9, no. 6, p. 1108, 2019.
     Google Scholar
  17. H. Su, E. Zio, J. Zhang, M. Xu, X. Li, and Z. Zhang, ?A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model,? Energy, vol. 178, pp. 585?597, 2019.
     Google Scholar
  18. C. Keerthisinghe, G. Verbic, and A. C. Chapman, ?A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning,? IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3291?3303, 2018.
     Google Scholar
  19. Y. Wang, D. Gan, M. Sun, N. Zhang, Z. Lu, and C. Kang, ?Probabilistic individual load forecasting using pinball loss guided LSTM,? Applied Energy, vol. 235, pp. 10?20, 2019.
     Google Scholar
  20. B. Yildiz, J. I. Bilbao, J. Dore, and A. B. Sproul, ?Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon,? Renewable Energy and Environmental Sustainability, vol. 3, p. 3, 2018.
     Google Scholar
  21. W. Kong, Z. Y. Dong, D. J. Hill, F. Luo, and Y. Xu, ?Short-Term Residential Load Forecasting Based on Resident Behaviour Learning,? IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1087?1088, 2018.
     Google Scholar
  22. M. Imani and H. Ghassemian, ?Electrical Load Forecasting Using Customers Clustering and Smart Meters in Internet of Things,? 2018 9th International Symposium on Telecommunications (IST), 2018.
     Google Scholar
  23. P. Guo, J. C. Lam, and V. O. Li, ?Drivers of domestic electricity users? price responsiveness: A novel machine learning approach,? Applied Energy, vol. 235, pp. 900?913, 2019.
     Google Scholar
  24. W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, ?Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network,? IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841?851, 2019.
     Google Scholar
  25. ?Energy Efficiency Trends in Canada 1990 to 2013,? Natural Resources Canada, 31-Jan-2019. [Online]. Available: https://www.nrcan.gc.ca/energy/publications/19030. [Accessed: 15-Apr-2020].
     Google Scholar
  26. F. Wang, Y. Yu, Z. Zhang, J. Li, Z. Zhen, and K. Li, ?Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting,? Applied Sciences, vol. 8, no. 8, p. 1286, Jan. 2018.
     Google Scholar
  27. Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, ?Dropout: A Simple Way to Prevent Neural Networks from Overfitting,? Journal of Machine Learning Research. Vol. 15, pp. 1929-1958, 2014.
     Google Scholar
  28. Yarin, Ghahramani, and Zoubin, ?A Theoretically Grounded Application of Dropout in Recurrent Neural Networks,? arXiv.org, 05-Oct-2016. [Online]. Available: https://arxiv.org/abs/1512.05287. [Accessed: 15-Apr-2020].
     Google Scholar
  29. A. Graves and J. Schmidhuber, ?Framewise phoneme classification with bidirectional LSTM and other neural network architectures,? Neural Networks, 19-Aug-2005. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0893608005001206. [Accessed: 15-Apr-2020].
     Google Scholar
  30. A. Graves, N. Jaitly, and A.-R. Mohamed, ?Hybrid speech recognition with Deep Bidirectional LSTM,? 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013.
     Google Scholar
  31. S. Wang, X. Wang, S. Wang, and D. Wang, ?Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting,? International Journal of Electrical Power & Energy Systems, vol. 109, pp. 470?479, 2019.
     Google Scholar
  32. J. Kim and N. Moon, ?BiLSTM model based on multivariate time series data in multiple field for forecasting trading area,? Journal of Ambient Intelligence and Humanized Computing, 2019.
     Google Scholar
  33. K. B. Sahay, S. Sahu, and P. Singh, ?Short-term load forecasting of Toronto Canada by using different ANN algorithms,? 2016 IEEE 6th International Conference on Power Systems (ICPS), 2016.
     Google Scholar


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