Paper Info
Title
Adaptive Learning in Multiagent Systems: A Forecasting Methodology Based on Error Analysis.
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.
Year
DOI
Venue
2012
10.1007/978-3-642-28762-6_42
HIGHLIGHTS ON PRACTICAL APPLICATIONS OF AGENTS AND MULTI-AGENT SYSTEMS
Keywords
Field
DocType
Adaptive Learning,Electricity Markets,Error Analysis,Forecasting Methods,Information Theory,Multiagent Systems
Information theory,Electricity market,Electricity,Computer science,Operations research,Strategic bidding,Multi-agent system,Artificial intelligence,Artificial neural network,Adaptive learning,Machine learning,Profit (economics)
Conference
Volume
ISSN
Citations 
156
1867-5662
2
PageRank 
References 
Authors
0.46
6
5
Name
Order
Citations
PageRank
Tiago M. Sousa119322.35
Tiago William Pinto2126.98
Zita A. Vale339085.67
Isabel Praça421240.45
Hugo Morais525731.41