Abstract
A short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of utmost importance for the planning of facilities, optimization of day-to-day operations, and an effective management of the available resources. In the context of energy and telecommunication networks, the load data are usually represented as real-valued time series characterized by strong temporal dependencies and seasonal patterns. We begin by reviewing several methods that have been adopted in the past years for the task of short-term load forecast and we highlight their main advantages and limitations. We then introduce the framework of recurrent neural networks, a particular class of artificial neural networks specialized in the processing of sequential/temporal data. We explain how recurrent neural networks can be an effective tool for prediction, especially in those cases where the extent of the time dependencies is unknown a-priori.
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References
Bianchi FM, De Santis E, Rizzi A, Sadeghian A (2015a) Short-term electric load forecasting using echo state networks and PCA decomposition. IEEE Access 3:1931–1943. https://doi.org/10.1109/ACCESS.2015.2485943
Bianchi FM, Kampffmeyer M, Maiorino E, Jenssen R (2017) Temporal overdrive recurrent neural network. arXiv preprint arXiv:170105159
Bianchi FM, Scardapane S, Uncini A, Rizzi A, Sadeghian A (2015b) Prediction of telephone calls load using echo state network with exogenous variables. Neural Netw 71:204–213. https://doi.org/10.1016/j.neunet.2015.08.010
Box GE, Cox DR (1964) An analysis of transformations. J R Stat Soc Ser B Methodol 211–252
Box GE, Jenkins GM, Reinsel GC (2011) Time series analysis: forecasting and control, vol 734. Wiley
Bunn DW (2000) Forecasting loads and prices in competitive power markets. Proc IEEE 88(2)
Claveria O, Monte E, Torra S (2015) Tourism demand forecasting with neural network models: different ways of treating information. Int J Tour Res 17(5):492–500. https://doi.org/10.1002/jtr.2016,jTR-13-0416.R2
Claveria O, Torra S (2014) Forecasting tourism demand to catalonia: Neural networks vs. time series models. Econo Modell 36:220–228. https://doi.org/10.1016/j.econmod.2013.09.024
Dang-Ha TH, Bianchi FM, Olsson R (2017) Local short term electricity load forecasting: automatic approaches. arXiv:1702:08025
Daz-Robles LA, Ortega JC, Fu JS, Reed GD, Chow JC, Watson JG, Moncada-Herrera JA (2008) A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of temuco, chile. Atmos Environ 42(35):8331–8340. https://doi.org/10.1016/j.atmosenv.2008.07.020
Deihimi A, Showkati H (2012) Application of echo state networks in short-term electric load forecasting. Energy 39(1):327–340
Deihimi A, Orang O, Showkati H (2013) Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57:382–401
Flunkert V, Salinas D, Gasthaus J (2017) DeepAR: probabilistic forecasting with autoregressive recurrent networks. arXiv:1704:04110
Gers FA, Eck D, Schmidhuber J (2001) Applying lstm to time series predictable through time-window approaches. In: Dorffner G, Bischof H, Hornik K (eds) Artificial Neural Networks—ICANN 2001: 2001 Proceedings International Conference Vienna, Austria, August 21–25. Springer, Berlin, Heidelberg, pp 669–676. https://doi.org/10.1007/3-540-44668-0_93
Gooijer JGD, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22(3):443–473. https://doi.org/10.1016/j.ijforecast.2006.01.001 (twenty five years of forecasting)
Graves A (2011) Practical variational inference for neural networks. In: Advances in neural information processing systems, pp 2348–2356
Graves A (2012) Sequence transduction with recurrent neural networks. arXiv preprint arXiv:12113711
Graves A (2013) Generating sequences with recurrent neural networks, pp 1–43. arXiv preprint arXiv:13080850, arXiv:1308.0850v5
Graves A, Liwicki M, Bunke H, Schmidhuber J, Fernández S (2008) Unconstrained on-line handwriting recognition with recurrent neural networks. In: Advances in Neural Information Processing Systems, pp 577–584
Graves A, Schmidhuber J (2009) Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in neural information processing systems, pp 545–552
Graves A, Wayne G, Danihelka I (2014) Neural turing machines. arXiv:1410.5401
Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2015) LSTM: a search space odyssey. arXiv preprint arXiv:150304069
Gregor K, Danihelka I, Graves A, Rezende DJ, Wierstra D (2015) Draw: A recurrent neural network for image generation. arXiv preprint arXiv:150204623
Hippert H, Pedreira C, Souza R (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55. https://doi.org/10.1109/59.910780
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Hyndman R, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media
Jan van Oldenborgh G, Balmaseda MA, Ferranti L, Stockdale TN, Anderson DL (2005) Did the ECMWF seasonal forecast model outperform statistical ENSO forecast models over the last 15 years? J Clim 18(16):3240–3249
Jang JSR (1993) Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Kon SC, Turner LW (2005) Neural network forecasting of tourism demand. Tour Econ 11(3):301–328. https://doi.org/10.5367/000000005774353006
Kourentzes N (2013) Intermittent demand forecasts with neural networks. Int J Prod Econ 143(1):198–206. https://doi.org/10.1016/j.ijpe.2013.01.009
Law R (2000) Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tour Manag 21(4):331–340. https://doi.org/10.1016/S0261-5177(99)00067-9
Malhotra P, Vig L, Shroff G, Agarwal P (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings. Presses universitaires de Louvain, p 89
Mikolov T (2012) Statistical language models based on neural networks. PhD thesis, Brno University of Technology
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
Oord Avd, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: A generative model for raw audio. arXiv preprint arXiv:160903499
Palmer A, Montao JJ, Ses A (2006) Designing an artificial neural network for forecasting tourism time series. Tour Manag 27(5):781–790. https://doi.org/10.1016/j.tourman.2005.05.006
Pascanu R, Mikolov T, Bengio Y (2013b) On the difficulty of training recurrent neural networks. In: Proceedings of the 30th International Conference on International Conference on Machine Learning, JMLR.org, ICML’13, vol 28, pp III–1310–III–1318. http://dl.acm.org/citation.cfm?id=3042817.3043083
Peng Y, Lei M, Li JB, Peng XY (2014) A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting. Neural Comput Appl 24(3–4):883–890
Plummer E (2000) Time series forecasting with feed-forward neural networks: guidelines and limitations. Neural Netw 1:1
Ruiz PA, Gross G (2008) Short-term resource adequacy in electricity market design. IEEE Trans Power Syst 23(3):916–926
Sapankevych NI, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24–38
Schäfer AM, Zimmermann HG (2007) Recurrent neural networks are universal approximators. Int J Neural Syst 17(04):253–263. https://doi.org/10.1142/S0129065707001111
Schäfer AM, Zimmermann HG (2007) Recurrent Neural Networks are universal approximators. Int J Neural Syst 17(4):253–263. https://doi.org/10.1142/S0129065707001111
Shen H, Huang JZ (2008) Interday forecasting and intraday updating of call center arrivals. Manuf Serv Oper Manag 10(3):391–410
Simchi-Levi D, Simchi-Levi E, Kaminsky P (1999) Designing and managing the supply chain: concepts, strategies, and cases. McGraw-Hill, New York
Sutskever I, Martens J, Hinton GE (2011) Generating text with recurrent neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 1017–1024
Takens F (1981) Detecting strange attractors in turbulence. Springer
Taylor JW (2008) A comparison of univariate time series methods for forecasting intraday arrivals at a call center. Manag Sci 54(2):253–265
Teixeira JP, Fernandes PO (2012) Tourism time series forecast-different ANN architectures with time index input. Procedia Technol 5:445–454. https://doi.org/10.1016/j.protcy.2012.09.049
Tsaur SH, Chiu YC, Huang CH (2002) Determinants of guest loyalty to international tourist hotelsa neural network approach. Tour Manag 23(4):397–405. https://doi.org/10.1016/S0261-5177(01)00097-8
Vermaak J, Botha EC (1998) Recurrent neural networks for short-term load forecasting. IEEE Trans Power Syst 13(1):126–132. https://doi.org/10.1109/59.651623
Weston J, Chopra S, Bordes A (2014) Memory networks. arXiv:1410.3916
Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks:: the state of the art. Int J Forecast 14(1):35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
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Bianchi, F.M., Maiorino, E., Kampffmeyer, M.C., Rizzi, A., Jenssen, R. (2017). Introduction. In: Recurrent Neural Networks for Short-Term Load Forecasting. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-70338-1_1
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