Modeling the electric power consumption in a multi-area system

European Transactions on Electrical Power

Volumn 21, Issue 1, 2011, Pages 413-423

  Perninge, Magnus ^a   Knazkins, Valerijs ^a   Amelin, Mikael ^a   Söder, Lennart ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

continuous time; electric power consumption; mean reversion; Nordic power system; stochastic modeling

Indexed keywords

CONTINUOUS TIME; ELECTRIC POWER CONSUMPTION; MEAN REVERSION; NORDIC POWER SYSTEM; STOCHASTIC MODELING;

CONTINUOUS TIME SYSTEMS; DIFFERENTIAL EQUATIONS; ELECTRIC UTILITIES; ELECTRICITY; ENERGY UTILIZATION; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

EID: 79551500194     PISSN: 1430144X     EISSN: 15463109     Source Type: Journal    
DOI: 10.1002/etep.450     Document Type: Article

References (22)

1. Misiorek A, Weron R,. Modeling and forecasting electricity loads: a comparison. In The European Electricity Market EEM-04, 2004.
2. Davis R, Brockwell P,. Introduction to Time Series and Forecasting. Springer: New York, 2002.
3. CAISO.
4. Alfuhaid AS, El-Sayed MA,. Cascaded artificial neural network for short-term load forecasting. IEEE Transactions on Power Systems 1997; 12: 1749-1755.
5. Olsson M,. On Optimal Hydropower Bidding in Systems with Wind Power. PhD Thesis, Royal Institute of Technology (KTH), May 2009. TRITA-EE 2009: 021.
6. Borodin AN, Salminen P,. Handbook of Brownian Motion-Facts and Formulae, 2nd edn. Birkhäuser: Basel, Switzerland, 2002.
7. Bibby BM, SÃrensen M,. Martingale estimation functions for discretely observed diffusion processes. Bernoulli 1990; 1 (I/II): 17-39.
8. Perninge M, Knazkins V, Amelin M, Söder L,. Estimating upper confidence bounds of electric power consumption. Proceedings of MEPCON, 2009.
9. Nowicka-Zagrajekb J, Weron R, Koz lowskab B,. Modeling electricity loads in California: a continuous-time approach. Physica A 2001; 299: 344-350.
10. Ãksendal B,. Stochastic Differential Equations, 6th edn. Springer: Berlin, Heidelberg, 2005.
11. Chung KL, Williams RJ,. Introduction to Stochastic Integration. Birkhäuser: Boston, 1983.
12. Uhlenbeck GE, Ornstein LS,. On the theory of Brownian motion. Physical Review 1930; 36: 823-841.
13. Dornier F, Queruel M,. Caution to the wind. Energy & Power Risk Management 2000; 17: 30-32.
14. Alaton P, Djehiche B, Stillberger D,. On modelling and pricing weather derivatives. Applied Mathematical Finance 2002; 9: 1-20.
15. Chen W-K, Chiang M-H, Cheng C-H,. Option pricing in Ornstein-Uhlenbeck position process: the application in the impact of price limits.
16. Barndorff-Nielsen OE,. Processes of normal inverse Gaussian type. Finance and Stochastics 1998; 2: 41-68.
17. Jeantheau T, Laredo C, Genon-Catalot V,. Parameter estimation for discretely observed stochastic volatility models. Bernoulli 1998; 4: 283-303.
18. Barndorff-Nielsen OE, Shephard N,. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society Series B 2002; 64: 133-165.
19. Bollerslev T, Zhou. H,. Estimating volatility diffusion using conditional moments of integrated volatility. Journal of Econometrics 2002; 109: 33-65.
20. Ditlevsen S, SÃrensen M,. Inference for observations of integrated diffusion processes. Scandinavian Journal of Statistics 2004; 31: 417-429.
21. 5 141-160 Box, G.E.P. Use and abuse of regression. Technometrics 1966; 8 (8): 625-629.
22. SÃrensen M,. Prediction-based estimating functions. The Econometrics Journal 2000; 3: 123-147.