- title = "Dynamical model reconstruction and accurate prediction of power-pool time series",
- author = "Vinay Varadan and Henry Leung and Eloi Bosse",
- journal = "IEEE Transactions on Instrumentation and Measurement",
- volume = "55",
- number = "1",
- month = feb,
- year = "2006",
- pages = "327--336",
- keywords = "genetic algorithms, genetic programming, Lyapunov methods, chaos, delay estimation, fractals, least squares approximations, nonlinear dynamical systems, power markets, prediction theory, time series, Lyapunov-dimension calculation, Lyapunov-spectrum, attractor-dimension, chaos, correlation-dimension calculation, delay embedding, delay estimation, dynamical model reconstruction, embedding dimension, fractal dimension, fractal-dimension estimates, least squares genetic programming, local state-space predictor, low-dimensional chaotic dynamical system, nonlinear dynamics, nonlinear time-series analysis, nonlinearity tests, power price, power-pool demand, power-pool time series prediction, prediction analysis, radial basis function neural network, stationarity tests, Chaos, Lyapunov exponents, fractal dimension, GP, local prediction, nonlinear time-series analysis, power price and demand prediction, power-pool time series, radial basis function (RBF) neural net",
- ISSN = "0018-9456",
-
DOI = "
doi:10.1109/TIM.2005.861492",
- size = "10 pages",
- abstract = "The emergence of the power pool as a popular institution for trading of power in different countries has led to increased interest in the prediction of power demand and price. We investigate whether the time series of power-pool demand and price can be modelled as the output of a low-dimensional chaotic dynamical system by using delay embedding and estimation of the embedding dimension, attractor-dimension or correlation-dimension calculation, Lyapunov-spectrum and Lyapunov-dimension calculation, stationarity and nonlinearity tests, as well as prediction analysis. Different dimension estimates are consistent and show close similarity, thus increasing the credibility of the fractal-dimension estimates. The Lyapunov spectrum consistently shows one positive Lyapunov exponent and one zero exponent with the rest being negative, pointing to the existence of chaos. The authors then propose a least squares genetic programming (LS-GP) to reconstruct the nonlinear dynamics from the power-pool time series. Compared to some standard predictors including the radial basis function (RBF) neural network and the local state-space predictor, the proposed method does not only achieve good prediction of the power-pool time series but also accurately predicts the peaks in the power price and demand based on the data sets used in the present study.",
-
notes = "INSPEC Accession Number:8768025
Dept. of Electr. Eng., Columbia Univ., New York, NY, USA",
