Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook

Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system modeling leading to increased computational requirements. Furthermore, with complexity, uncertainty increases likewise calling for probabilistic assessments and scenario analyses. To adequately and efficiently address these various requirements, new methods from the field of data science are needed to accelerate current methods. With our systematic literature review, we want to close the gap between the three disciplines (1) assessment of security of electricity supply, (2) artificial intelligence, and (3) design of experiments. For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities as promising fields of application that have not sufficiently been covered, yet. We end with deriving a new methodological pipeline for adequately and efficiently addressing the present and upcoming challenges in the assessment of security of electricity supply.

[1]  Daryoush Habibi,et al.  A real-time energy management strategy for pumped hydro storage systems in farmhouses , 2020, Journal of Energy Storage.

[2]  Corinna Cortes,et al.  Boosting Decision Trees , 1995, NIPS.

[3]  Mario Schneider.  Statistische Versuchsplanung , 2020, Datenanalyse für Naturwissenschaftler, Mediziner und Ingenieure.

[4]  Yeming Dai,et al.  A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization , 2020 .

[5]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[6]  Oliver Kramer,et al.  Dimensionality Reduction with Unsupervised Nearest Neighbors , 2013, Intelligent Systems Reference Library.

[7]  Sancho Salcedo-Sanz,et al.  Feature selection in machine learning prediction systems for renewable energy applications , 2018, Renewable and Sustainable Energy Reviews.

[8]  Ravinesh C. Deo,et al.  Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms , 2019, Applied Energy.

[9]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

[10]  P. Praus.  Water quality assessment using SVD-based principal component analysis of hydrological data , 2006 .

[11]  Fernando Berzal Galiano,et al.  Evaluation Metrics for Unsupervised Learning Algorithms , 2019, ArXiv.

[12]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[13]  Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory , 2020, Energies.

[14]  David A. Cohn,et al.  Training Connectionist Networks with Queries and Selective Sampling , 1989, NIPS.

[15]  Yacine Rezgui,et al.  Electrical load forecasting models: A critical systematic review , 2017 .

[16]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[17]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[18]  Detlef Stolten,et al.  Impact of different time series aggregation methods on optimal energy system design , 2017, ArXiv.

[19]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[20]  Yoshua Bengio,et al.  Training Methods for Adaptive Boosting of Neural Networks , 1997, NIPS.

[21]  Joel Emer,et al.  Efficient Processing of Deep Neural Networks , 2020, Synthesis Lectures on Computer Architecture.

[22]  Ning Lu,et al.  A Supervised Machine Learning Approach to Control Energy Storage Devices , 2019, IEEE Transactions on Smart Grid.

[23]  R. Saidur,et al.  Application of support vector machine models for forecasting solar and wind energy resources: A review , 2018, Journal of Cleaner Production.

[24]  Yu Peng,et al.  Anomaly detection based on data stream monitoring and prediction with improved Gaussian process regression algorithm , 2014, 2014 International Conference on Prognostics and Health Management.

[25]  Michel Verleysen,et al.  The Curse of Dimensionality in Data Mining and Time Series Prediction , 2005, IWANN.

[26]  Mohamed Abdel-Nasser,et al.  Accurate photovoltaic power forecasting models using deep LSTM-RNN , 2017, Neural Computing and Applications.

[27]  Stefan Hirschberg,et al.  Comprehensive resilience assessment of electricity supply security for 140 countries , 2020, Ecological Indicators.

[28]  Jingyan Wang,et al.  Forecast of Power Grid Investment Scale Based on Support Vector Machine , 2020 .

[29]  Frédéric Magoulès,et al.  Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method , 2012 .

[30]  Lars Nolting,et al.  Are complex energy system models more accurate? An intra-model comparison of power system optimization models , 2019, Applied Energy.

[31]  Yoshio Kajitani,et al.  Simulation of operational reliability of thermal power plants during a power crisis: Are we underestimating power shortage risk? , 2018, Applied Energy.

[32]  Andrew McCallum,et al.  Toward Optimal Active Learning through Sampling Estimation of Error Reduction , 2001, ICML.

[33]  Hoon Heo,et al.  Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach , 2015 .

[34]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.

[35]  Lizhe Wang,et al.  A Survey on Active Deep Learning: From Model-driven to Data-driven , 2021, 2101.09933.

[36]  Marta Poncela Blanco,et al.  Generation Adequacy of the European Electricity System , 2018, 2018 15th International Conference on the European Energy Market (EEM).

[37]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[38]  Erik B. Sudderth.  Introduction to statistical machine learning , 2016 .

[39]  Sonia Yeh,et al.  Formalizing best practice for energy system optimization modelling , 2017 .

[40]  Kenny Q. Ye.  Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments , 1998 .

[41]  D. Stolten,et al.  Energy Storage as Part of a Secure Energy Supply , 2017 .

[42]  L. Breiman.  Heuristics of instability and stabilization in model selection , 1996 .

[43]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[44]  Robert W. Blanning,et al.  The construction and implementation of metamodels , 1975 .

[45]  Lior Rokach,et al.  Pattern Classification Using Ensemble Methods , 2009, Series in Machine Perception and Artificial Intelligence.

[46]  H. Sebastian Seung,et al.  Query by committee , 1992, COLT '92.

[47]  Jack P. C. Kleijnen,et al.  A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation” , 1975 .

[48]  Hamidreza Zareipour,et al.  A Price-Maker/Price-Taker Model for the Operation of Battery Storage Systems in Electricity Markets , 2019, IEEE Transactions on Smart Grid.

[49]  Bruno Storti,et al.  Improving the efficiency of a Savonius wind turbine by designing a set of deflector plates with a metamodel-based optimization approach , 2019, Energy.

[50]  Shouxiang Wang,et al.  A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network , 2019, Applied Energy.

[51]  Liz Varga,et al.  Energy and complexity: New ways forward , 2015 .

[52]  Ali Ouni,et al.  Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting , 2020, Energies.

[53]  Hossein Sangrody,et al.  Long term forecasting using machine learning methods , 2018, 2018 IEEE Power and Energy Conference at Illinois (PECI).

[54]  Beatriz de la Iglesia,et al.  Experimental evaluation of cluster quality measures , 2013, 2013 13th UK Workshop on Computational Intelligence (UKCI).

[55]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[56]  Robert W. Blanning.  The Sources and Uses of Sensitivity Information , 1974 .

[57]  J. Friedman.  Stochastic gradient boosting , 2002 .

[58]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[59]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[60]  Ronald L. Iman,et al.  Risk methodology for geologic disposal of radioactive waste: small sample sensitivity analysis techniques for computer models, with an application to risk assessment , 1980 .

[61]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[62]  Li-ion batteries for peak shaving, price arbitrage, and photovoltaic self-consumption in commercial buildings: A Monte Carlo Analysis , 2021 .

[63]  Stefan Vögele,et al.  Hydro-climatic conditions and thermoelectric electricity generation – Part I: Development of models , 2013 .

[64]  Jianchun Peng,et al.  A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.

[65]  Thomas J. Santner,et al.  Algorithms for Generating Maximin Latin Hypercube and Orthogonal Designs , 2011 .

[66]  R. Shahsavari,et al.  Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants , 2020 .

[67]  Rasmus Lund Jensen,et al.  A comparison of six metamodeling techniques applied to building performance simulations , 2018 .

[68]  A. O. Melodi,et al.  Long term load forecasting for Nigeria's electric power grid using ann and fuzzy logic models , 2017, 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON).

[69]  Rudolf Mathar,et al.  Convolutional Neural Network Based Fault Location Detector for Power Grids , 2019, 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech).

[70]  Preeti Arora,et al.  Analysis of K-Means and K-Medoids Algorithm For Big Data , 2016 .

[71]  Ira Assent,et al.  Clustering high dimensional data , 2012 .

[72]  C. Winzer.  Conceptualizing Energy Security , 2012 .

[73]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[74]  Carmen L. T. Borges,et al.  Impact of the Combined Integration of Wind Generation and Small Hydropower Plants on the System Reliability , 2015, IEEE Transactions on Sustainable Energy.

[75]  Jian Ma,et al.  A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network , 2018, Energies.

[76]  Thomas Spiegel,et al.  Can energy system modeling benefit from artificial neural networks? Application of two-stage metamodels to reduce computation of security of supply assessments , 2020, Comput. Ind. Eng..

[77]  T. Oyama,et al.  Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique , 2020, Journal of the Operations Research Society of China.

[78]  P. Sopp.  Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[79]  Eklas Hossain,et al.  A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models , 2020, IEEE Access.

[80]  A. C. Cilliers,et al.  Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant , 2018, Annals of Nuclear Energy.

[81]  Qin Chen,et al.  Effect of Input Features on the Performance of the ANN-based Wind Power Forecasting , 2019, 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA).

[82]  Wei Pei,et al.  A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids , 2020, IEEE Access.

[83]  M. Aalabaf-Sabaghi.  An Introduction to Data, Everything You Need to Know About AI: Big Data and Data ScienceF.Corea2019Cham Springer Nature xvi + 132 pp., £58.99 ISBN 978‐3-030‐04468‐8 (e‐book) , 2020 .

[84]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[85]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Hyper-Parameter Tuning of a Decision Tree Induction Algorithm , 2016, 2016 5th Brazilian Conference on Intelligent Systems (BRACIS).

[86]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[87]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[88]  Iver Bakken Sperstad,et al.  The impact of flexible resources in distribution systems on the security of electricity supply: A literature review , 2020 .

[89]  Rafael Mendoza-Arriaga,et al.  Modeling Dependent Outages of Electric Power Plants , 2020, Oper. Res..

[90]  Zhang Chao,et al.  Short-term prediction of wind power based on deep Long Short-Term Memory , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[91]  Jörg Bendfeld,et al.  Meteorological Data Forecast using RNN , 2017, Int. J. Grid High Perform. Comput..

[92]  Adam R. Brandt,et al.  Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison , 2019, Applied Energy.

[93]  Lijun Wu,et al.  Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph , 2019, Energy Conversion and Management.

[94]  Thomas Palmé,et al.  Gas turbine sensor validation through classification with artificial neural networks , 2011 .

[95]  Yitao Liu,et al.  Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network , 2017 .

[96]  R. A. FISHER,et al.  The Design and Analysis of Factorial Experiments , 1938, Nature.

[97]  Verónica Bolón-Canedo,et al.  Recent Advances in Ensembles for Feature Selection , 2018, Intelligent Systems Reference Library.

[98]  Daniel Berrar,et al.  Encyclopedia of Bioinformatics and Computational Biology , 2017 .

[99]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[100]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[101]  J. I.  The Design of Experiments , 1936, Nature.

[102]  Bart De Moor,et al.  Hyperparameter Search in Machine Learning , 2015, ArXiv.

[103]  Dennis K. J. Lin,et al.  Construction of nested orthogonal latin hypercube designs , 2013 .

[104]  Yann LeCun,et al.  Modeles connexionnistes de l'apprentissage , 1987 .

[105]  Antonio J. Conejo,et al.  Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling , 2018, IEEE Transactions on Power Systems.

[106]  Cesare Furlanello,et al.  Exact Bagging with k-Nearest Neighbour Classifiers , 2004, Multiple Classifier Systems.

[107]  Ran Li,et al.  Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.

[108]  Muhammad Asif Zahoor Raja,et al.  A novel wavenets long short term memory paradigm for wind power prediction , 2020 .

[109]  Detlef Stolten,et al.  Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models , 2021, Applied Energy.

[110]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[111]  Jay Apt,et al.  A time-dependent model of generator failures and recoveries captures correlated events and quantifies temperature dependence , 2019 .

[112]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[113]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[114]  H. Bourlard,et al.  Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.

[115]  Gaurav Kumar,et al.  MATHEMATICS FOR MACHINE LEARNING , 2020, Journal of Mathematical Sciences & Computational Mathematics.

[116]  D. Sauer,et al.  The development of stationary battery storage systems in Germany – A market review , 2020, Journal of Energy Storage.

[117]  Christian Lang,et al.  Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures , 2019, ArXiv.

[118]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[119]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[120]  Lars Nolting,et al.  How to model European electricity load profiles using artificial neural networks , 2020 .

[121]  Priti Srinivas Sajja.  Illustrated Computational Intelligence - Examples and Applications , 2021, Studies in Computational Intelligence.

[122]  Yang Mao,et al.  A review of wind power forecasting & prediction , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[123]  Robert W. Blanning.  Response to Michel, Kleijnen and Permut , 1975 .

[124]  Kazem Zare,et al.  Optimal bidding and offering strategies of merchant compressed air energy storage in deregulated electricity market using robust optimization approach , 2018 .

[125]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[126]  Detlef Stolten,et al.  A Review on Time Series Aggregation Methods for Energy System Models , 2020, Energies.

[127]  Haiguo Tang,et al.  A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting , 2018, Adv. Eng. Informatics.

[128]  Antoine Tahan,et al.  Wind turbine power curve modelling using artificial neural network , 2016 .

[129]  Wenhu Chen,et al.  Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting , 2019, NeurIPS.

[130]  S. Jebaraja,et al.  A review of energy models , 2006 .

[131]  Qiaozhu Zhai,et al.  Economic Dispatch of Energy Storage System in Micro-grid , 2019 .

[132]  G. Ravi,et al.  Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization , 2015 .

[133]  Haitao Liu,et al.  When Gaussian Process Meets Big Data: A Review of Scalable GPs , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[134]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[135]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[136]  Kunihiko Fukushima,et al.  Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .

[137]  Tingting Zhu,et al.  Short-term wind speed forecasting using empirical mode decomposition and feature selection , 2016 .

[138]  Nur Ashida Salim,et al.  Long Term Load Forecasting using Grey Wolf Optimizer - Artificial Neural Network , 2019, 2019 7th International Conference on Mechatronics Engineering (ICOM).

[139]  Lars Nolting,et al.  Can we phase-out all of them? Probabilistic assessments of security of electricity supply for the German case , 2020 .

[140]  Alexandre Sava,et al.  Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data , 2018, 2018 IEEE International Conference on Industrial Technology (ICIT).

[141]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[142]  Reinaldo Castro Souza,et al.  Long Term Electricity Forecast: A Systematic Review , 2015, ITQM.

[143]  D. Basak,et al.  Support Vector Regression , 2008 .

[144]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[145]  Kenny Gruchalla,et al.  Enabling immersive engagement in energy system models with deep learning , 2019, Stat. Anal. Data Min..

[146]  Junyou Yang,et al.  Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks , 2020, Applied Energy.

[147]  David Cohn,et al.  Active Learning , 2010, Encyclopedia of Machine Learning.

[148]  Kapil Bakshi,et al.  Considerations for artificial intelligence and machine learning: Approaches and use cases , 2018, 2018 IEEE Aerospace Conference.

[149]  John R. Kitchin,et al.  Pybliometrics: Scriptable Bibliometrics using a Python Interface to Scopus , 2019, SoftwareX.

[150]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[151]  Xiaolei Liu,et al.  Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network , 2020, Energy.

[152]  Yongqian Liu,et al.  Clustering methods of wind turbines and its application in short-term wind power forecasts , 2014 .

[153]  Rui Castro,et al.  Wind power forecast using neural networks: Tuning with optimization techniques and error analysis , 2020 .

[154]  M. Genoese,et al.  Future security of power supply in Germany—The role of stochastic power plant outages and intermittent generation , 2018 .

[155]  Ricardo J. Bessa,et al.  Future Trends for Big Data Application in Power Systems , 2018 .

[156]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[157]  Russell R. Barton,et al.  Tutorial: Simulation metamodeling , 2015, 2015 Winter Simulation Conference (WSC).

[158]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[159]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[160]  Yong Shi,et al.  A review of data-driven approaches for prediction and classification of building energy consumption , 2018 .

[161]  C. Rindt,et al.  Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks , 2019, Applied Energy.

[162]  Yu Zhang,et al.  Short-term wind power forecasting approach based on Seq2Seq model using NWP data , 2020 .

[163]  E. Kuntsche,et al.  Multivariate Analysemethoden: Eine anwendungsorientierte Einführung. , 2001 .

[164]  M. Rosen,et al.  A review of energy storage types, applications and recent developments , 2020 .