Long-Term Electricity Load Forecasting Based On Cascade Forward Backpropagation Neural Network
Keywords:CFBNN, Long-Term Load Forecasting, MAPE, Neural Network,
AbstractNowadays, the Electrical System has an important role in all sectors of life. Electricity has a strategic role. Accuracy and reliability in electricity load forecasting is a great key that can help electricity companies in supplying electricity efficiency, hence, reducing wasted energy. In addition, electricity load forecasting can also help electricity companies to determine the purchase price and power generation. Long-term forecasting is a method of forecasting with a span of more than one year. The historical data will be a reference in solving the problems. This research propose the concept of cascade forward backpropagation for long-term load forecasting. The advantage of this concept is that it can accommodate non-linear conditions without ignoring the linear conditions. This study compared the results of the original data, Feed Forward Backpropagation Neural Network (FFBNN) and Cascade Forward Backpropagation Neural Network (CFBNN). The results were measured by comparing Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE).
T. Haida and S. Muto, “Regression based peak load forecasting using a transformation technique,” Power Systems, IEEE Transactions on, vol. 9, no. 4, , 1994, pp. 1788–1794.
J. W. Taylor, “An evaluation of methods for very short-term load forecasting using minute-by-minute British data,” International Journal of Forecasting, vol. 24, no. 4, 2008, pp. 645–658.
M. Kandil, S. M. El-Debeiky, and N. Hasanien, “ Long-term load forecasting for fast developing utility using a knowledgebased expert system,” Power Systems, IEEE Transactions on, vol. 17, no. 2, 2002 pp. 491–496.
Michael A.M, Nihan K, and Virginie L. “ Forecasting Indonesia's electricity load through 2030 and peak demandreductions from appliance and lighting efficiency,” Energy for Sustainable Development. Volume 49, April 2019, Pages 65-77
C.-H. Wang, G. Grozev, and S. Seo. “ Middle-long power load forecasting based on dynamic grey prediction and support vector machine,” Energy, 2012; 41: 313-325.
V. Bianco, O. Manca, and S. Nardini. “Electricity consumption forecasting in Italy using linear regression models,” Energy, 34, 2009. pp. 1413-1421,
L. Ghods, M. Kalantar. “Different methods of long-term electric load demand forecasting; a comprehensive review,” Iranian Journal of Electrical and Electronic Engineering ,7, 2011,pp. 249-259.
Luiz Friedrich, Afshin Afshari. “ Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables,” The 7th International Conference on Applied Energy – ICAE, 2015
Zheng Hua, Xie Li, and Zhang Li-zi. ‘Electricity price forecasting based on GARCH model in deregulated market,” International Power Engineering Conference, 2005.
Hao Chen, Fangxing Li, Qiulan Wan, and Yurong Wang.” Short term load forecasting using regime-switching GARCH models,” IEEE Power and Energy Society General Meeting, 2011
Hao Chen, Qiulan Wan, Fangxing Li, and Yurong Wang. “Short term load forecasting based on improved ESTAR GARCH model,” IEEE Power and Energy Society General Meeting, 2012.
S. R. Khuntia, J. L. Ruedam and M. A. M. M. van der Meijden. “Volatility in Electrical Load Forecasting for Longterm Horizon – An ARIMA-GARCH Approach,” International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2016.
Norizan Mohamed, Maizah Hura Ahmad, Zuhaimy Ismail, and Suhartono. “Short Term Load Forecasting Using Double Seasonal ARIMA Model,“ Proceedings of the Regional Conference on Statistical Sciences, 2010.
Norizan Mohamed, Maizah Hura Ahmad, Zuhaimy amd Ismail,Suhartono. “ Double Seasonal ARIMA Model for Forecasting Load Demand,” MATEMATIKA, Vol 26, No 2, 2010, pp.217–231
Shilpa.G.N, Dr.G.S.Sheshadri. “Short-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical Load,” International Journal of Engineering Research and Development, Volume 13, Issue 7,July 2017, pp.75-79.
D. Alberg, M. Last. “Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms,” Vietnam Journal of Computer Science, Vol.5, 2018, pp.241–249.
Wenqing Zhao, Fei Wang, and Dongxiao Niu. “The Application of Support Vector Machine in Load Forecasting,” Journal Of Computers, Vol. 7, No. 7, July 2012
Yangyang Fua, Zhengwei Lia, Hao Zhang, and Peng Xu. “Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices,” Procedia Engineering, 121, 2015, pp. 1016 – 1022.
C. Li, S. Li, and Y Liu. “A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting,” Appl Intell, Vol.45, 2016, pp.1166–1178.
N. Dongxiao, M. Tiannan, and L. Bingyi. “Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm,” Journal of Combinatorial Optimization, Vol.33, 2017, pp.1122–1143.
Yang L, Xiangying X, Liang S, Dayan M, and Deping Z. “Prediction of Electric Load for Users Based on BP Neural Network,” 4th International Conference on Systems, Computing, and Big Data (ICSCBD 2018), 2018.
Luis H, Carlos B, Javier M.A, Lorena C, Belén C,Antonio S, Francisco P, Ángel F, and Jaime L. “Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems,” Energies, 7, 2014, pp. 1576-1598.
G. Mohi Ud Din, A. K. Marnerides. “Short term power load forecasting using Deep Neural Networks,” International Conference on Computing, Networking and Communications (ICNC), 2017.
M. Rafiei, T. Niknam, J. Aghaei, M. Shafie-Khah, and João P. S. Catalão. “Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine,” IEEE Transactions on Smart Grid, Vol. 9, Issue. 6, Nov. 2018.
Nianyin Z, Hong Z, Weibo L, Jinling L, and Fuad E.A. “A switching delayed PSO optimized extreme learning machine for short-term load forecasting,” Neurocomputing, Volume 240, 31 May 2017, pp.175- 182.
M.R. AlRashidi, K.M. EL-Naggar. “Long term electric load forecasting based on particle swarm optimization,” Applied Energy, 87, 2010, pp.320–326.
D. Niu and S. Dai. “A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis,” Energies,Vol.10,Issue.3, 2017.
M. Emarati, F. Keynia, and A. Askarzadeh. “Application of hybrid neural networks combined with comprehensive learning particle swarm optimization to short term load forecasting,” Computational Intelligence in Electrical Engineering, Vol. 10, Issue.1, 2019.
Shahid M.A, Muhammad A, Zubair A.K, and Hassan S. “An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting,” Neural Computing and Applications, Volume 25, Issue 7–8, 2014, pp 1967–1978.
Feyza G,Celal Ö.P. “Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study,” Energy Systems,Volume 4, Issue 3, 2013, pp 289–300.
M. A. Mat Daut, M. Y. Hassan,H. Abdullah,H. A. Rahman, M. P. Abdullah, and F. Hussin. “An Improved Building Load Forecasting Method using a combined Least Square Support Vector Machine and modified Artificial Bee Colony,” Journal of Electrical Engineering, Vol. 16, No. 1, 2017, pp.1-5.
H. H. Çevik, H. Harmanc, and M. Çunkaş. “Short-term Load Forecasting based on ABC and ANN for Smart Grids,” International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue), 2016, pp.38–43.
Vitaly Schetinin. “An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms‖,” Computer Science Department, University of Exeter, Exeter, EX4 4QF, UK. 2005.
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