Abstract
Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Network, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.
The authors would like to acknowledge FCT, FEDER, POCTI, POSI, POCI, POSC, POTDC and COMPETE for their support to R&D Projects and GECAD Unit.
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Sousa, T.M., Pinto, T., Vale, Z., Praça, I., Morais, H. (2012). Adaptive Learning in Multiagent Systems: A Forecasting Methodology Based on Error Analysis. In: Pérez, J., et al. Highlights on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28762-6_42
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DOI: https://doi.org/10.1007/978-3-642-28762-6_42
Publisher Name: Springer, Berlin, Heidelberg
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