The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets

Lu, 1993, Neural network based short term load forecasting, IEEE Trans. Power Syst., 8, 336, 10.1109/59.221223 Kell, 2018, Segmenting residential smart meter data for short-term load forecasting T. Hong, J. Wilson, J. Xie, A. Member, Long term probabilistic load forecasting and normalization with hourly information 5 (1) (2014) 456–462. Al-Musaylh, 2018, Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia, Adv. Eng. Inform., 35, 1, 10.1016/j.aei.2017.11.002 Vrablecová, 2017, Smart grid load forecasting using online support vector regression, Comput. Electr. Eng., 1 S.-j. Huang, S. Member, K.-r. Shih, Short-Term Load Forecasting Via ARMA Model Identification Including Non-Gaussian 18 (2) (2003) 673–679. Andersen, 2013, Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers, Energy Convers. Manage., 68, 244, 10.1016/j.enconman.2013.01.018 Kell, 2019, ElecSim : Monte-Carlo open-source agent-based model to inform policy for long-term electricity planning, 556 A.J.M. Kell, M. Forshaw, A.S. McGough, Long-term electricity market agent based model validation using genetic algorithm based optimization, in: The Eleventh ACM International Conference on Future Energy Systems (e-Energy’20). A.K. Singh, . Ibraheem, S. Khatoon, M. Muazzam, D.K. Chaturvedi, Load forecasting techniques and methodologies: A review, in: ICPCES 2012-2012 2nd International Conference on Power, Control and Embedded Systems. Kell, 2018, Segmenting residential smart meter data for short-term load forecasting, 91 Ahmad, 2017, Trees vs. neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption, Energy Build., 147, 77, 10.1016/j.enbuild.2017.04.038 Chen, 2004, Load forecasting using support vector machines : A study on EUNITE competition 2001, IEEE Trans. Power Syst., 19, 1821, 10.1109/TPWRS.2004.835679 Kim, 2000, Short-term load forecasting for special days in anomalous load conditions using neural networks, IEEE Trans. Power Syst., 15, 559, 10.1109/59.867141 Nagi, 2008, Electrical power load forecasting using hybrid self-organizing maps and support vector machines, 51 Quilumba, 2014, 1 Nguyen, 2017, Short-term electricity load forecasting with Time Series Analysis, 214 Gross, 1987, Short-term load forecasting, Proc. IEEE, 75, 1558, 10.1109/PROC.1987.13927 M. Ghofrani, M. Hassanzadeh, M.S. Fadali, Smart Meter Based Short-Term Load Forecasting for Residential Customers 13–17. Fard, 2014, A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting, J. Exp. Theor. Artif. Intell., 26, 167, 10.1080/0952813X.2013.813976 Humeau, 2013, Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households Johansson, 2017, Operational demand forecasting in district heating systems using ensembles of online machine learning algorithms, Energy Procedia, 116, 208, 10.1016/j.egypro.2017.05.068 Y. Baram, R. El-Yaniv, K. Luz, Online choice of active learning algorithms, in: Proceedings, Twentieth International Conference on Machine Learning, Vol. 1, 2003, pp. 19–26. Schmitt, 2008, Adapting the user context in realtime: Tailoring online machine learning algorithms to ambient computing, Mob. Netw. Appl., 13, 583, 10.1007/s11036-008-0095-8 Widmer, 1996, Learning in the presence of concept drift and hidden contexts, Mach. Learn., 23, 69, 10.1007/BF00116900 Pindoriya, 2008, An adaptive wavelet neural network-based energy price forecasting in electricity markets, IEEE Trans. Power Syst., 23, 1423, 10.1109/TPWRS.2008.922251 Goncalves Da Silva, 2014, The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading, IEEE Trans. Smart Grid, 5, 402, 10.1109/TSG.2013.2278868 K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, Y. Singer, Online passive-aggressive algorithms, J. Mach. Learn. Res. doi: http://dx.doi.org/10.1201/b15810-63. Box, 1964, An analysis of transformations, J. Source Stat. R. Ser. Soc., 26, 211 Forgy, 1965, Cluster analysis of multivariate data: Efficiency versus interpretability of classification, Biometrics, 21, 768 Hinton, 1989, Connectionist learning procedures, Artificial Intelligence, 40, 185, 10.1016/0004-3702(89)90049-0 Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol., 58, 267 Geladi, 1993, vol. 12, 224 1994, Price: £27.50, J. Chemometr., 8, 371 Friedman, 2010, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw., 33, 1, 10.18637/jss.v033.i01 Efron, 1988, Least angle regression, Ann. Statist., 32, 440 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. System Sci., 55, 119, 10.1006/jcss.1997.1504 J.H. Friedman, Greedy function approximation: A gradient boosting machine (316) 400. Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Pedregosa, 2011, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825 Creme, https://pypi.org/project/creme/. Quinlan, 1993 Shu, 2006, Short-term load forecasting based on an adaptive hybrid method, Power Syst. IEEE Trans., 21, 392, 10.1109/TPWRS.2005.860944 Drucker, 1997, Support vector regression machines, Adv. Neural Inf. Process. Syst., 1, 155 Smola, 2004, A tutorial on support vector regression, Stat. Comput., 14, 199, 10.1023/B:STCO.0000035301.49549.88 Akaike, 1974, A new look at the statistical model identification, IEEE Trans. Automat. Control, 19, 716, 10.1109/TAC.1974.1100705 Pao, 2007, Forecasting electricity market pricing using artificial neural networks, Energy Convers. Manage., 48, 907, 10.1016/j.enconman.2006.08.016 J. Ma, L.K. Saul, S. Savage, G.M. Voelker, Identifying suspicious URLs: An application of large-scale online learning, in: Proceedings of the 26th International Conference On Machine Learning, ICML 2009, 2009, pp. 681–688. 2019 Department for Business Energy & Industrial Strategy, Companies house - gov.uk, UK Government. Department for Business Energy & Industrial Strategy, Updated energy and emissions projections 2018, The Energy White Paper (April). Elexon portal and Sheffield University, G.b. national grid status, https://www.gridwatch.templar.co.uk/. 2019, 0