Empirical Mode Decomposition with Random Forest Model Based Short Term Load Forecasting
DOI:
https://doi.org/10.13052/dgaej2156-3306.37411Keywords:
Empirical mode decomposition, ensemble learning, random forest, short term load forecasting, error metrics.Abstract
This paper presents a hybrid methodology for improving load forecasting in electric power networks by combining the time-frequency data analysis method based on Empirical Mode Decomposition (EMD) with the Random Forest (RF) technique. The performance of the hybrid EMD-RF model is tested on real-time load data of Bengaluru city, Karnataka (India) from 01st January 2019 to 30th June 2019. An ensemble empirical mode decomposition is applied to decompose original load data into various signals known as intrinsic mode functions (IMF). The meteorological variables (MV) such as moisture content, dew point, dry bulb temperature, humidity, and solar irradiance (SR) are also taken into consideration for the day ahead seasonal STLF. The decomposed signals are further analysed using the ensemble learning-based Random Forest (RF) technique. The result obtained from the model is aggregated to obtain the final forecasted result. The superiority of the hybrid EMD-RF model is established through a comparative statistical error analysis with other non-decomposition and decomposition methods based on EMD-Bagging, EMD-ANN, Artificial Neural Network (ANN), Bagging, and Random Forest (RF).
Downloads
References
Y. Al-Rashid and L.D. Paarmann, “Short-term electric load forecasting using neural network models,” Circuits and Syst., Ames, IA, 1996, pp. 1436–1439.
Medha Joshi, Rajiv Singh, “Short-term load forecasting approaches: A review”, International Journal of Recent Engineering Research and Development (IJRERD) Volume No. 01 – Issue No. 03, ISSN: 2455-8761, pp. 09–17.
Swarpoor R., Hussien A. Abdelqader, “load forecasting for power system planning and operation using artificial neural network at albatinah region oman,” Journal of Engineering Science and Technology Vol. 7, No. 4 (2012) 498–504.
G. T. Heinemann, D. A. Nordmian, E. C. Plant. “The relationship between summer weather and summer loads – a regression analysis”, IEEE Transactions on Power Apparatus and Systems, vol. PAS-85, no. 11, pp. 1144–1154, Nov. 1966.
Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011 ISSN (Online): 1694–0814.
Tao Hong, David A. Dickey.: Electric load forecasting: fundamentals and best practices (2009).
J. H. Park, Y. M. Park and K. Y. Lee, “Composite Modeling for Adaptive Short-Term Load Forecasting,” IEEE Transaction Power System, vol. 6, no. 2, pp. 450–457, May 1991.
H. M. Al-Hamadi and S. A. Soliman, “Long-term/mid-term electric load forecasting based on short-term correlation and annual growth,” Electr. Power Syst. Res., vol. 74, no. 3, pp. 353–361, 2005.
S. Farzana, M. Liu, A. Baldwin, and M. U. Hossain, “Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China,” Energy Build., vol. 81, pp. 161–169, Oct. 2014.
R. P. Broadwater, A. Sargent, A. Yarali, H. E. Shaalan and J. Nazarko, “Estimating Substation Peaks from Research Data, IEEE Transaction on Power Delivery, vol. 12, pp. 451–456, 1997.
S. G. N and G. S. Sheshadri, 2020, Electrical Load Forecasting Using Time Series Analysis, IEEE Bangalore Humanitarian Technology Conference (B-HTC), Vijiyapur, India, 2020, pp. 1–6, doi: 10.1109/B-HTC50970.2020.9297986.
B. Nepal, M. Yamaha, A. Yokoe, T. Yamaji, 2020, Electricity load forecasting using clustering and ARIMA model for energy management in buildings. JpnArchit Rev.; 3: 62–76. https://doi.org/10.1002/2475-8876.12135.
S. J. Huang and K. R. Shih, 2003, Short-term load forecasting via ARMA model identification including non-Gaussian process considerations, IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673–679, May 2003.
J. Y. Fan and J. D. McDonald, “A real-time implementation of short term load forecasting for distribution power systems,” IEEE Trans. Power Syst., vol. 9, no. 2, pp. 988–994, May 1994.
Marco Cococcioni, Eleonora D’Andrea and Beatrice Lazzerini “One day-ahead forecasting of energy production in solar photovoltaic installations: An empirical study,” Intelligent Decision Technologies 6, pp. 197–210, 2012.
Mandeep Singh, Raman Maini, “Various Electricity Load Forecasting Techniques with Pros and Cons”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277–3878, Volume-8 Issue-6, March 2020.
G. Dudek, P. Pełka and S. Smyl, 2020, A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.3046629.
Taylor, J.W., 2003. “Short-term electricity demand forecasting using double seasonal exponential smoothing,” Journal of the Operational Research Society, 44(0): 799–805.
Taylor, J.W., 2012. “Short-Term Load Forecasting With Exponentially Weighted Methods,” IEEE Transactions on Power Systems, 27(1): 458–464.
McKenzie, E. and E.S. Gardner Jr, 2010. “Damped trend exponential smoothing: A modelling viewpoint,” International Journal of Forecasting, 26(4): 661–665.
Ming-Guang Zhang, “Short-term load forecasting based on support vector machines regression,” 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China, 2005, pp. 4310–4314 Vol. 7, doi: 10.1109/ICMLC.2005.1527695.
M. S. Li, J. L. Wu, T. Y. Ji, Q. H. Wu and L. Zhu, “Short-term load forecasting using Support Vector Regression-based Local Predictor,” 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 2015, pp. 1–5, doi: 10.1109/PESGM.2015.7285911.
L. Hu, L. Zhang, T. Wang and K. Li, 2020, Short-term load forecasting based on support vector regression considering cooling load in summer, 2020 Chinese Control and Decision Conference (CCDC), Hefei, China, pp. 5495–5498, doi: 10.1109/CCDC49329.2020.9164387.
Chia-Nan Ko and Cheng-Ming Lee. Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended kalman filter. Energy, 49:413–422, 2013.
Glauber Souto dos Santos, Luiz Guilherme Justi Luvizotto, Viviana Cocco Mariani, and Leandro dos Santos Coelho. Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process. Expert Systems with Applications, 39(5):4805–4812, 2012.
T. Czernichow, A. Piras, K. Imhof, P. Caire, Y. Jaccard, B. Dorizzi, and A. Germond, “Short term electrical load forecasting with artificial neural networks,” Engineering Intelligent Syst., vol. 2, pp. 85–99, 1996.
Peng T, Hubele N, Karady G. Advancement in the application of neural networks for short term load forecasting. IEEE Trans Power Syst 1992;7(1):250–8.
Yao S, Song Y, Zhang L, Cheng X. Wavelet transform and neural networks for short term electrical load forecasting. Energ Convers Manage 2000;41:1975–88.
Tang, X., Dai, Y., Wang, T. and Chen, Y., 2019, Short−
term power load forecasting based on multi-layer bidirectional recurrent neural network. IET Gener. Transm. Distrib., 13: 3847–3854. https://doi.org/10.1049/iet-gtd.2018.6687.
Muhammad Buhari and Sunusi Sani Adamu, “Short-Term Load Forecasting Using Artificial Neural Network,” International Multi Conference of Engineers and Computer Scientists., vol. 1, pp. 442–449, March 2012.
Huang, C-J, Shen, Y, Chen, Y-H, Chen, H-C. A novel hybrid deep neural network model for short-term electricity price forecasting. Int J Energy, Res. 2021; 45: 2511–2532. https://doi.org/10.1002/er.5945
Arjun Baliyan, Kumar Gaurav and Sudhansu Kumar Mishra, “A Review of Short Term Load Forecasting using Artificial Neural Network Models,” Procedia Computer Science, 2015, vol. 48, pp. 121–125.
Srashti Shrivastava and Dr. Krishna Teerth Chaturvedi, “A Review of Artificial Intelligence Techniques for Short Term Electric Load Forecasting,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 7, Issue 5, pp. 2241–2247, May 2018.
Mastorocostas P, Theocharis J, Kiartzis S, Bakirtzis A. A hybrid fuzzy modeling method for short-term load forecasting. Math Comput Simulat 2000;51:221–32.
Sokratis Papadopoulos, Ioannis Karakatsanis, “Short-term Electricity Load Forecasting using Time Series and Ensemble Learning Methods”, publication/282939702, March 2015.
Melih Yucesan, Engin Pekel, Erkan Celik, Muhammet Gul and Faruk Serin (2021) Forecasting daily natural gas consumption with regression, time series and machine learning based methods, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, DOI: 10.1080/15567036.2021.1875082.
Sewell, M. Ensemble Learning; Technical Report; University College London: London, UK, 2008.
Zhou, Z.H. Ensemble Methods: Foundations and Algorithms, 1st ed.; Chapman & Hall CRC: Boca Raton, FL, USA, 2012.
Zhang, C.; Ma, Y. Ensemble Machine Learning: Methods and Applications; Springer Science & Business Media: Boston, MA, USA 2012.
Hakan Acikgoz, Ceyhun Yildiz and Mustafa Sekkeli (2020) An extreme learning machine based very short-term wind power forecasting method for complex terrain, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42:22, 2715–2730, DOI: 10.1080/15567036.2020.1755390.
Breiman, L. (1996b) Bagging predictors. Machine Learning, vol. 24, no. 2, pp. 123–140.
J. Vaish, S. S. Datta and K. Seethalekshmi, 2020, Short Term Load Forecasting using ANN and Ensemble Models Considering Solar Irradiance, IEEE International Conference on Electrical and Electronics Engineering (ICE3), Gorakhpur, India, pp. 44–48, doi: 10.1109/ICE348803.2020.9122986.
J. Vaish, S. S. Datta and k. Seethalekshmi, 2021, Short-term Load Forecasting using Bootstrap Aggregation based Ensemble Method, IEEE 7th International Conference on Electrical Energy Systems (ICEES), Chennai, India, pp. 245–249, doi: 10.1109/ICEES51510.2021.9383755.
Grzegorz Dudek, “Short-Term Load Forecasting Using Random Forests”, Intelligent Systems’2014: Proceedings of the 7th IEEE International Conference Intelligent Systems IS’2014, September 24–26, 2014, Warsaw, Poland, Volume 2: Tools, Architectures, Systems, Applications (pp. 821–828).
A. Lahouar, J. Ben Hadj Slama, “Day-ahead load forecast using random forest and expert input selection”, Energy Conversion and Management; ISSN 0196–8904; Worldcat; 2015; v. 103; pp. 1040–1051.
Federico Divina, Aude Gilson, Francisco Goméz-Vela, Miguel García Torres and José F. Torres, “Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting,” energies 2018, MDPI, Published: 16 April 2018, pp. 1–31.
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms. MIT Press, 2000.
C. Guan, P. B. Luh, L. D. Michel, Y. Wang, and P. B. Friedland, “Very short-term load forecasting: wavelet neural networks with data prefiltering,” IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 30–41, 2013.
R.-A. Hooshmand, H. Amooshahi, and M. Parastegari, “A hybrid intelligent algorithms based short-term load forecasting approach,” International Journal of Electrical Power & Energy Systems, vol. 45, pp. 313–324, 2013.
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.- C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,” in Roy. Soc. London A, vol. 454, 1998, pp. 903–995.
M. Alhamdoosh and D. Wang, “Fast decorrelated neural network ensembles with random weights,” Information Sciences, vol. 264, pp. 104–117, 2014.
X. Zhang, K. K. Lai, and S.-Y. Wang, “A new approach for crude oil price analysis based on Empirical Mode Decomposition,” Energy Economics, vol. 30, no. 3, pp. 905–918, 2008.
M. Hu and H. L. Liang, “Adaptive multiscale entropy analysis of multivariate neural data,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 12–15, 2012.
Y. Wei and M.-C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, 2012.
J. Wang, W. Zhang, Y. Li, J. Wang, and Z. Dang, “Forecasting wind speed using empirical mode decomposition and ELMAN neural network,” Applied Soft Computing, vol. 23, pp. 452–459, 2014.
C.-F. Chen, M.-C. Lai, and C.-C. Yeh, “Forecasting tourism demand based on empirical mode decomposition and neural network,” Knowledge-Based Systems, vol. 26, pp. 281–287, 2012.
Z.-H. Zhu, Y.-L. Sun, and Y. Ji, “Short-term load forecasting based on EMD and SVM,” High Voltage Engineering, vol. 33, no. 5, pp. 118–122, 2007.
C. S. Lai et al., 2020, “Multi-view Neural Network Ensemble for Short and Mid-term Load Forecasting,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.3042389.
L. Wang, S. Mao and B. Wilamowski, 2019, Short-Term Load Forecasting with LSTM Based Ensemble Learning, International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 793–800, doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00145.
Z. Tang, G. Zhao, G. Wang and T. Ouyang, 2020, “Hybrid Ensemble Framework for Short-Term Wind Speed Forecasting,” in IEEE Access, vol. 8, pp. 45271–45291, doi: 10.1109/ACCESS.2020.2978169.
J. Bedi and D. Toshniwal, 2018, Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting, IEEE Access, vol. 6, pp. 49144–49156, doi: 10.1109/ACCESS.2018.2867681.
Fan, G-F, Guo, Y-H, Zheng, J-M, Hong, W-C., 2020, A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back-propagation neural network for mid-short-term load forecasting. Journal of Forecasting; 39: 737–756. https://doi.org/10.1002/for.2655
H. Jun et al., 2020, A Novel Short-term Residential Load Forecasting Model Combining Machine Learning Method with Empirical Mode Decomposition, Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, pp. 816–820, doi: 10.1109/AEEES48850.2020.9121467.
S. Fan, L. Chen and W. Lee, 2009, Short-Term Load Forecasting Using Comprehensive Combination Based on Multi meteorological Information, IEEE Transactions on Industry Applications, vol. 45, no. 4, pp. 1460–1466, July-Aug., doi: 10.1109/TIA.2009.2023571.
W. Chu, Y. Chen, Z. Xu and W. Lee, “Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity,” in IEEE Transactions on Industry Applications, vol. 47, no. 1, pp. 232–237, Jan.-Feb. 2011, doi: 10.1109/TIA.2010.2090440.
Takilalte, S. Harrouni and J. Mora (2019) Forecasting global solar irradiance for various resolutions using time series models – case study: Algeria, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, DOI: 10.1080/15567036.2019.1649756.
Song Li, Lalit Goel, Peng Wang, 2016, An ensemble approach for short-term load forecasting by extreme learning machine, Applied Energy, Volume 170, Pages 22–29, ISSN 0306–2619, https://doi.org/10.1016/j.apenergy.2016.02.114.
Z.-H. Zhou, 2012, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: Chapman and Hall/CRC.
P. Kumkar, I. Madan, A. Kale, O. Khanvilkar and A. Khan, 2018, Comparison of Ensemble Methods for Real Estate Appraisal, IEEE 3rd International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 297–300, doi: 10.1109/ICICT43934.2018.9034449.
Angel Colmenares and Jianzhou Wang (2021) Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, DOI: 10.1080/15567036.2021.1922550.
Ying-Ying Cheng, P. P. K. Chan and Zhi-Wei Qiu, 2012, Random forest-based ensemble system for short term load forecasting, International Conference on Machine Learning and Cybernetics, pp. 52–56, doi: 10.1109/ICMLC.2012.6358885.
Xiaoyu Wu, Jinghan He, Tony Yip, Jian lu and Ning Lu, “A two-stage random forest method for short-term load forecasting,” 2016 IEEE Power and Energy Society General Meeting (PESGM), 2016, pp. 1–5, doi: 10.1109/PESGM.2016.7741295.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984: Classification and Regression Trees. Chapman and Hall (1984).
Y. Xuan et al.,2021, Multi-Model Fusion Short-Term Load Forecasting Based on Random Forest Feature Selection and Hybrid Neural Network, IEEE Access, vol. 9, pp. 69002–69009, 2021, doi: 10.1109/ACCESS.2021.3051337.
Website Online: http://218.248.45.137:8282/LoadCurveUpload/lcdownloadview.asp
