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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the ?vertical? dimension, long short-term memory (LSTM) neural networks and...
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Arne Vogler and Florian Ziel
The present paper considers the problem of choosing among a collection of competing electricity price forecasting models to address a stochastic decision-making problem. We propose an event-based evaluation framework applicable to any optimization proble...
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Stephen Haben, Julien Caudron and Jake Verma
The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will req...
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Anna Manowska
The impact of environmental regulations introduced by the European Union is of key importance for electricity generation systems. The Polish fuel structure of electricity production is based on solid fuels. Moreover, the generating base is outdated and m...
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Sergey Voronin and Jarmo Partanen
A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and p...
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