Dr Stephen Haben
Address
Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Main:
20) Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations, Stephen Haben, Siddharth Arora, Georgios Giasemidis, Marcus Voss, Danica Vukadinovic Greetham, Applied Energy, 2021. [Preprint] [Link]
19) Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain, Stephen Haben, Julien Caudron, Jake Verma, Forecasting, vol. 3, 2021. [Link]
18) Dirichlet Sampled Capacity and Loss Estimation for LV Distribution Networks with Partial Observability, R. Telford, B. Stephen, J Browell and S. Haben, IEEE Trans. on Power Delivery, 2020. [Link]
17) A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads, F. Alasali, S. Haben, H. Foudeh, and W. Holderbaum, Energies, vol. 13, 2020. [Link]
16) Stochastic Optimal Energy Management System for RTG Cranes Network Using Genetic Algorithm and Ensemble Forecasts, F. Alasali, S. Haben and W. Holderbaum, Journal of Energy Storage, vol. 24, 2019. [Link]
15) Short Term Load Forecasts of Low Voltage Demand and the Effects of Temperature, S. Haben, G. Giasemidis, F. Ziel and S. Arora, International Journal of Forecasting, vol. 35, 2019. [Preprint] [Link]
14) Energy Management Systems for a network of electrified cranes with energy storage, F. Alasali, S. Haben and W. Holderbaum, International Journal of Electrical Power and Energy Systems, 2018. [Link]
13) Modelling the Demand and Uncertainty of Low Voltage Networks and the Effect of non-Domestic Consumers, G. Giasemidis and S. Haben, Sustainable Energy, Grids and Networks, 2018.
12) The conditioning of least squares problems in variational data assimilation, J.M. Tabeart, S.L. Dance, S.A. Haben, A.S. Lawless, N.K. Nichols and J.A. Waller, vol. 25, Numerical Linear Algebra with Applications, 2018. [Link]
11) Day-ahead industrial load forecasting for electric RTG cranes, F. Alasali, S. Haben, V. Becerra and W. Holderbaum, Journal of Modern Power Systems and Clean Energy, 2018. [Link]
10) A Genetic Algorithm Approach for Modelling Low Voltage Network Demands, G. Giasemidis, S. A. Haben, T. Lee, C. Singleton. and P. Grindrod, Applied Energy, vol. 203, pp. 463--473, 2017. [Preprint] [Link]
9) Optimal energy management and MPC strategies for electrified RTG cranes with energy storage systems, F. Alasali, S. Haben, V. Becerra, and W. Holderbaum, Energies, vol. 10, 2017. [Link]
8) A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting, S. Haben and G. Giasemidis, International Journal of Forecasting, vol. 32, pp. 1017--1022, 2016. [Preprint] [Link]
7) Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data, S. Haben, C. Singleton, P. Grindrod, IEEE Trans. Smart Grid, vol. 7, pp. 136--144, 2016. [Preprint] [Link]
6) Long term individual load forecast under different electrical vehicles uptake scenarios, A. Poghosyan, D. V. Greetham, S. Haben and T. Lee, Applied Energy, vol. 157, pp. 699--709, 2015. [Preprint] [Link]
5) A New Error Measure for Forecasts of Household-level, High Resolution Electrical Energy Consumption, S. Haben, J. A. Ward, D. V.Greetham, P. Grindrod and C. Singleton, International Journal of Forecasting, vol. 30, pp. 246--256, 2014. [Link] [Preprint]
4) A Peak Reduction Scheduling Algorithm For Storage Devices On The Low Voltage Network, M. Rowe, T. Yunusov, S. Haben, C. Singleton, W. Holderbaum and B. Potter, IEEE Transactions on Smart Grid, vol. 5, pp. 2115--2124, 2014. [Link]
3) The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction, M. Rowe, T. Yunusov, S. Haben, W. Holderbaum, B. Potter, Energies, vol. 7, pp. 3537-3560, 2014. [Link]
2) Conditioning of incremental variational data assimilation, with application to the Met Office system, S. A. Haben, A.S. Lawless and N.K.Nichols, Tellus A, vol. 63, pp. 782--792, 2011. [Link] [Preprint]
1) Conditioning and preconditioning of the variational data assimilation problem, S. A. Haben, A.S. Lawless and N. K. Nichols, Computers & Fluids, vol. 46, pp. 252--256, 2011. [Link]
Conference proceedings:
6) Peak power reduction for electrified Rubber-Tyred Gantry (RTG) cranes using electric storage, F. Alasali, S. Haben, V. Becerra, and W. Holderbaum, Proceedings for 8th International Symposium on Automatic control (AUTSYM) Sept, 2017. [Link]
5) Evaluating the effectiveness of storage control in reducing peak demand on low voltage feeders, T Yunusov, S Haben, T Lee, F Ziel, W Holderbaum, B Potter, 24th International Conference & Exhibition on Electricity Distribution (CIRED), Glasgow, 2017. [Preprint][Link]
4) Electric vehicles and low voltage grid: impact of uncontrolled demand side response, L Hattam, D Vukadinovic Greetham, S Haben, D Roberts, 24th International Conference & Exhibition on Electricity Distribution (CIRED), Glasgow, 2017. [Preprint][Link]
3) Analysis of RTG Crane Load Demand and Short-term Load Forecasting, Feras Alasali , Stephen Haben, Victor Becerra, William Holderbaum, International Congress on Advances in Engineering and Technological Developments (AETD-16), Int. J. Computing, Communications & Instrumentation Engg. (IJCCIE) Vol. 3, 2016 [Link]
2) Modelling the weekly electricity consumption of small to medium enterprises, T. Lee, S. A. Haben and P. Grindrod. 18th European Conference on Mathematics for Industry, ECMI 2014. [Preprint] [Link]
1) Mathematical Solutions for Electricity Networks in a Low Carbon Future, 22nd International Conference on Electricity Distribution (CIRED), Stockholm, 10-13 June 2013. [Link]
Book Chapters:
1) Smart Storage Scheduling and Forecasting for Peak Reduction on Low-Voltage Feeders, Timur Yunusov, Georgios Giasemidis and Stephen Haben, In: Kahraman C., Kayakutlu G. (eds) Energy Management—Collective and Computational Intelligence with Theory and Applications. Studies in Systems, Decision and Control, Springer, 2018. [Link]
2) Distribution Network Demand and Its Uncertainty, Stephen Haben and Georgios Giasemidis, Ali Arefi et al. (Eds): Electric Distribution Network Management and Control, Springer, 2018. [Book]
Preprints:
A Probabilistic Framework for Forecasting Household Energy Demand Profiles, N. Charlton, S. A. Haben, P. Grindrod, D. V. Greetham and C. Singleton. [preprint]
Other:
Conditioning and Preconditioning of the Minimisation Problem in Variational Data Assimilation, PhD Thesis, University of Reading, 2011. [Link]
Using Mathematics to Respond to Changes in Energy Demand in a Future Low Carbon UK, S. Haben, Mathematics of Planet Earth: Mathematics today special Issue, Institute of Mathematics and its Applications, Feb 2013.
Mathematics Matters: A smarter future for next generation local electricity networks, Mathematics Today, Institute of Mathematics and its Applications, Feb 2015. [Link]
Point and Probabilistic Load Forecasting methods for electricity demand (espercially low voltage), optimization methods (including for control of storage devices), Unsupervised discrimination (Finite mixture models, k-means, etc.), time series analysis, data assimilation, linear algebra, forecasting measures, smart grid, data analytics, clustering methods.
Former project: New Thames Valley Vision, 2011-2017, for more details see here.