Modeling and forecasting electricity prices with input/output hidden Markov models

By Alicia Mateo González, Antonio Muñoz San Roque, Javier García-González. IEEE Transactions on Power Systems, 2005

318 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6687cafd717c96bcdb5867d3d09b1bf4627b5170

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In Electrical and Electronic Engineering

Citations in the corpus

Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market (2008, methodology): “In this paper, the input/output hidden Markov model (IOHMM) approach presented in [13] is applied in order to generate electricity price scenarios.”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, methodology): “[10] MLP, DRM IOHMM 27, 1, 15, 6, 7, 8 MLP – 5, DRM – 4 –”
Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques (2007, methodology): “Electricity price models can be broadly classified in two sets [14], namely production cost models and statistical models.”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, unknown): “Hamilton (1989), for instance, suggests an autoregressive process of higher order for both regimes, while Bierbrauer et al.”
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method (2007, methodology): “For example, time-series [7], ARIMA model [11], GARCH model [2], artiﬁcial neural networks (ANN) [5], [10], [12]–[14], wavelet transform and ARIMA models [23], agents-based simulations [16], input-output hidden Markov models (IOHMM) [15], fuzzy neural network [24], etc.”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “…to model the evolution of time series generated from a discrete or discretized state space (in contrast with a continuous state space) where the switching between the finite state follows a definite pattern (e.g., electricity price fluctuation due to competitor strategies; Gonzalez et al. (2005)).”
Day-ahead price forecasting of electricity markets by a new fuzzy neural network (2006, methodology): “Finally, in [5], it is concluded that it is often very hard for an NN to extract a global model for the whole data of the MCP series.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, unknown): “Serinaldi (2011), Gonzalez et al. (2012), Wu et al.”
Hierarchical Convolutional Deep Learning in Computer Vision (2013, background): “This includes motionstyle transfer [53], economic forecasting with external indicators [86], and various tasks in natural language processing [24].”