Personal Website:
+44 1865 615219
Address
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Research Group:
Research Interests:
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.
Major / Recent Publications:
Main:
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]
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]
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]
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]
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]
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]
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]
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]
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:
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]
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]
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]
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]
Mathematical Solutions for Electricity Networks in a Low Carbon Future, 22nd International Conference on Electricity Distribution (CIRED), Stockholm, 10-13 June 2013. [Link]
Preprints:
Conditioning and Preconditioning of the Optimal State Estimation Problem, S. A. Haben, A. S. Lawless and N.K.Nichols. [Preprint]
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]