Stephen Haben
Cited by
Cited by
Analysis and clustering of residential customers energy behavioral demand using smart meter data
S Haben, C Singleton, P Grindrod
IEEE Transactions on Smart Grid 7 (1), 136-144, 2016
A new error measure for forecasts of household-level, high resolution electrical energy consumption
S Haben, J Ward, D Vukadinovic Greetham, C Singleton, P Grindrod
International Journal of Forecasting 30 (2), 246-256, 2014
A peak reduction scheduling algorithm for storage devices on the low voltage network
M Rowe, T Yunusov, S Haben, C Singleton, W Holderbaum, B Potter
IEEE Transactions on Smart Grid 5 (4), 2115-2124, 2014
A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting
S Haben, G Giasemidis
International Journal of Forecasting 32 (3), 1017-1022, 2016
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 7 (6), 3537-3560, 2014
Short term load forecasting and the effect of temperature at the low voltage level
S Haben, G Giasemidis, F Ziel, S Arora
International Journal of Forecasting, 2019
Conditioning of incremental variational data assimilation, with application to the Met Office system
SA Haben, AS Lawless, NK Nichols
Tellus A: Dynamic Meteorology and Oceanography 63 (4), 782-792, 2011
Conditioning and preconditioning of the variational data assimilation problem
SA Haben, AS Lawless, NK Nichols
Computers & Fluids 46 (1), 252-256, 2011
Energy management systems for a network of electrified cranes with energy storage
F Alasali, S Haben, W Holderbaum
International Journal of Electrical Power & Energy Systems 106, 210-222, 2019
Long term individual load forecast under different electrical vehicles uptake scenarios
A Poghosyan, DV Greetham, S Haben, T Lee
Applied Energy 157, 699-709, 2015
Conditioning and preconditioning of the minimisation problem in variational data assimilation
SA Haben
PhD thesis, Department of Mathematics and Statistics, University of Reading, 2011
Review of Low-Voltage Load Forecasting: Methods, Applications, and Recommendations
S Haben, S Arora, G Giasemidis, M Voss, DV Greetham
Applied Energy 304, 2021
Day-ahead industrial load forecasting for electric RTG cranes
F Alasali, S Haben, V Becerra, W Holderbaum
Journal of Modern Power Systems and Clean Energy 6 (2), 223-234, 2018
Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems
F Alasali, S Haben, V Becerra, W Holderbaum
Energies 10 (10), 1598, 2017
The conditioning of least‐squares problems in variational data assimilation
JM Tabeart, SL Dance, SA Haben, AS Lawless, NK Nichols, JA Waller
Numerical Linear Algebra with Applications 25 (5), e2165, 2018
A genetic algorithm approach for modelling low voltage network demands
G Giasemidis, S Haben, T Lee, C Singleton, P Grindrod
Applied Energy 203, 463-473, 2017
Conditioning of the 3DVAR data assimilation problem
SA Haben, AS Lawless, NK Nichols
University of Reading, Dept. of Mathematics, Math Report Series 3, 2009, 2009
Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts
F Alasali, S Haben, W Holderbaum
Journal of Energy Storage 24, 100759, 2019
Analysis of RTG crane load demand and short-term load forecasting
F Alasali, S Haben, V Becerra, W Holderbaum
Int J Comput Commun Instrumen Eng 3 (2), 448-454, 2016
Modelling the electricity consumption of small to medium enterprises
TE Lee, SA Haben, P Grindrod
European Consortium for Mathematics in Industry, 341-349, 2014
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