Electrical load forcasting in intraday markets using neural network based on long-short term memory
156 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.82.2022.91-97Keywords:
Time series, intraday electricity market, recurrent neural network (RNN), long-short-term-memory (LSTM).Abstract
Load forecasting plays an important role for buyers participating into electricity markets. Buyers need to be able to forecast load demand 15 minutes in advance for participating in the intraday market. Thus, the problem of predicting the electricity load 15 minutes in advance plays a vital role in participating into the intraday market. Due to the non-linearity and instability under natural conditions of electrical loads in small-scale power systems, accurate forecasting is still a challenge. This paper investigates the use of Long-Short-Term-Memory (LSTM) short-term memory structure based on feedback neural network structure to predict the electricity load 15 minutes in advance.
References
[1]. P. Shinde and M. Amelin, “A Literature Review of Intraday Electricity Markets and Prices”, 2019 IEEE Milan PowerTech, pp. 1-6, (2019), doi: 10.1109/PTC.2019.8810752. DOI: https://doi.org/10.1109/PTC.2019.8810752
[2]. A. Khan and M. Rizwan, "ANN and ANFIS Based Approach for Very Short Term Load Forecasting: A Step Towards Smart Energy Management System," 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), (2021). DOI: https://doi.org/10.1109/SPIN52536.2021.9566146
[3]. H. Aoyang, Z. Shengqi, J. Xuehui and Z. Zhisheng, "Short-term Load Forecasting Model Based on RBF Neural Network Optimized by Artificial Bee Colony Algorithm," 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), (2021). DOI: https://doi.org/10.1109/ICBAIE52039.2021.9390043
[4]. M. Bashari and A. Rahimi-Kian, "Forecasting Electric Load by Aggregating Meteorological and History-based Deep Learning Modules," 2020 IEEE Power & Energy Society General Meeting (PESGM), (2020). DOI: https://doi.org/10.1109/PESGM41954.2020.9282124
[5]. M. Kumar and R. Krishan, "Non-Linear Auto-Regressive Modeling based Day-ahead BESS Dispatch Strategy for Distribution Transformer Overload Management," 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), (2021). DOI: https://doi.org/10.1109/ICECCE52056.2021.9514132
[6]. M. Khashei and M. Bijari, “A novel hybridization of artificial neural networks and arima models for time series forecasting,” Applied Soft Computing, vol. 11, no. 2, pp. 2664–2675, (2011). DOI: https://doi.org/10.1016/j.asoc.2010.10.015
[7]. K.-Y. Chen and C.-H. Wang, “A hybrid sarima and support vector machines in forecasting the production values of the machinery industry in taiwan,” Expert Systems with Applications, vol. 32, no. 1, pp. 254–264, (2007). DOI: https://doi.org/10.1016/j.eswa.2005.11.027
[8]. C.-L. Hor, S. J. Watson, and S. Majithia, “Daily load forecasting and maximum demand estimation using arima and garch,” in Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. IEEE international Conf. pp. 1–6, (2006).
[9]. K. M. Kam, “Stationary and non-stationary time series prediction using state space model and pattern-based approach”. The University of Texas at Arlington, (2014).
[10]. M. Lineesh, K. Minu, and C. J. John, “Analysis of nonstationary nonlinear economic time series of gold price: A comparative study,” in International Mathematical Forum, vol. 5, no. 34. Citeseer, pp. 1673–1683, (2010).
[11]. YanyingLi, JinxingChe, YoulongYang, “Subsampled support vector regression ensemble for short term electric load forecasting”, Energy Volume 164, Pages 160-170, (2018). DOI: https://doi.org/10.1016/j.energy.2018.08.169
[12]. A. Navot, L. Shpigelmen, N.Tishby, E.Vaadia, “Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity”, Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8], (2005).
[13]. H. S. Hippert, C. E. Pedreira and R. C. Souza, “Neural Networks for ShortTerm Load Forecasting: a Review and Evaluation”, IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44-55, (2001). DOI: https://doi.org/10.1109/59.910780
[14]. Kunjin Chen, Kunlong Chen, Qin Wang, Ziyu He, Jun Hu, Jinliang He, “Short-Term Load Forecasting With Deep Residual Networks”, IEEE Transactions on Smart Grid, Volume: 10, Issue: 4, (2019). DOI: https://doi.org/10.1109/TSG.2018.2844307
[15]. P. Mandal, T. Senjyu, N. Urasaki and T. Funabashi, “A neural network based saveral-hour-ahead electric load forecasting using similar days approach”, Elec. Power and Energy Systems, vol. 28, pp. 367-373, (2006). DOI: https://doi.org/10.1016/j.ijepes.2005.12.007
[16]. Ying Chen, Peter B. Luh, Che Guan, Yige Zhao, Laurent D. Michel, Matthew A. Coolbeth, Peter B. Friedland, “Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks”, IEEE Transactions on Power Systems, Volume: 25, Issue: 1, (2010). DOI: https://doi.org/10.1109/TPWRS.2009.2030426
[17]. https://www.terna.it/en/electric-system/transparency-report/total-load.
