96,411 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionāmaking process as well as strategic planning. In this study, a prototype asymmetricābased neuroāfuzzy network (AGFINN) architecture has been implemented for shortāterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellāestablished learningābased models
Forecasting day-ahead electricity prices in Europe: the importance of considering market integration
Motivated by the increasing integration among electricity markets, in this
paper we propose two different methods to incorporate market integration in
electricity price forecasting and to improve the predictive performance. First,
we propose a deep neural network that considers features from connected markets
to improve the predictive accuracy in a local market. To measure the importance
of these features, we propose a novel feature selection algorithm that, by
using Bayesian optimization and functional analysis of variance, evaluates the
effect of the features on the algorithm performance. In addition, using market
integration, we propose a second model that, by simultaneously predicting
prices from two markets, improves the forecasting accuracy even further. As a
case study, we consider the electricity market in Belgium and the improvements
in forecasting accuracy when using various French electricity features. We show
that the two proposed models lead to improvements that are statistically
significant. Particularly, due to market integration, the predictive accuracy
is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage
error). In addition, we show that the proposed feature selection algorithm is
able to perform a correct assessment, i.e. to discard the irrelevant features
Neural Network Ensembles for Time Series Prediction
Rapidly evolving businesses generate massive
amounts of time-stamped data sequences and defy a demand
for massively multivariate time series analysis. For such data
the predictive engine shifts from the historical auto-regression
to modelling complex non-linear relationships between multidimensional
features and the time series outputs. In order to
exploit these time-disparate relationships for the improved time
series forecasting, the system requires a flexible methodology
of combining multiple prediction models applied to multiple
versions of the temporal data under significant noise component
and variable temporal depth of predictions. In reply
to this challenge a composite time series prediction model
is proposed which combines the strength of multiple neural
network (NN) regressors applied to the temporally varied
feature subsets and the postprocessing smoothing of outputs
developed to further reduce noise. The key strength of the model
is its excellent adaptability and generalisation ability achieved
through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006
and NN3 Competition 2007 concerning prediction of univariate
and multivariate time-series. It showed the best predictive
performance among 12 competitive models in the NISIS 2006
and is under evaluation within NN3 2007 Competition
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