{"type":"ScholarlyArticle","title":"AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations","authors":[{"name":"Tiago Pinto","slug":"tiago-pinto","given":"Tiago","family":"Pinto","affiliation":""},{"name":"Zita Vale","slug":"zita-vale","given":"Zita","family":"Vale","affiliation":""}],"abstract":"This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).","doi":"10.24963/ijcai.2019/957","url":"https://ijcai.org/proceedings/2019/957","pdf":"https://ijcai.org/proceedings/2019/0957.pdf","markdown":"https://ijcai.org/proceedings/2019/957.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/957","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":957,"pages":{"first":6563,"last":6565},"keywords":["Applications: Energy","AI: AI Modelling and Simulation","AI: Machine Learning","AI: Multiagent Systems"],"videos":[],"references":[{"position":1,"raw":"[Chan and Ortiz, 2018] Hau Chan and Luis E. Ortiz (2018). Learning Game-theoretic Models from Aggregate Be- havioral Data with Applications to Vaccination Rates in Public Health. In Proc. of the 17th International Confer- ence on Autonomous Agents and Multiagent Systems (AAMAS 2018)","title":"Learning Game-theoretic Models from Aggregate Behavioral Data with Applications to Vaccination Rates in Public Health","authors":"Hau Chan, Luis E Ortiz","year":"2018","venue":"Proc. of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Grau-Moya et al., 2018] Jordi Grau-Moya, Haitham Bou- Ammar, and Felix Leibfried (2018). Balancing Two- Player Stochastic Games with Soft Q-Learning. in Proc. of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018)","title":"Balancing Two-Player Stochastic Games with Soft Q-Learning","authors":"Jordi Grau-Moya, Haitham Bou-Ammar, Felix Leibfried","year":"2018","venue":"Proc. of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018)","doi":null,"url":null,"internal_url":"/proceedings/2018/37"},{"position":3,"raw":"[Niu, et al., 2018] Lei Niu, Fenghui Ren, and Minjie Zhang (2018). Feasible Negotiation Procedures for Multiple In- terdependent Negotiations. in Proc. of the 17th Interna- tional Conference on Autonomous Agents and Multia- gent Systems (AAMAS 2018)","title":"Feasible Negotiation Procedures for Multiple Interdependent Negotiations","authors":"Lei Niu, Fenghui Ren, Minjie Zhang","year":"2018","venue":"Proc. of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Odeh et al., 2018] Rodrigo Pérez Odeh, David Watts, and Matias Negrete-Pincetic (2018). Portfolio applications in electricity markets review: Private investor and manager perspective trends, Renew. Sustain. Energy Rev., 81, 192-204.","title":"Portfolio applications in electricity markets review: Private investor and manager perspective trends","authors":"Rodrigo Pérez Odeh, David Watts, Matias Negrete-Pincetic","year":"2018","venue":"Renew. Sustain. Energy Rev","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Pang et al., 2018] Yue. Pang, Bo Yao, Xiangdong Zhou, Yong Zhang, Yiming Xu, and Zijing Tan (2018). Hierar- chical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Con- sistency. in Proc. of the 27th International Joint Confer- ence on Artificial Intelligence (IJCAI 2018)","title":"Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency","authors":"Yue, Bo Pang, Xiangdong Yao, Yong Zhou, Yiming Zhang, Zijing Xu, Tan","year":"2018","venue":"Proc. of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018)","doi":null,"url":null,"internal_url":"/proceedings/2018/487"},{"position":6,"raw":"[Pinto et al., 2014] Tiago Pinto, Zita Vale, Tiago M. Sousa, Isabel Praça, Gabriel Santos, and Hugo Morais (2014). Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation. Integr. Comput. Eng., 21(4), 399-415","title":"Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation","authors":"Tiago Pinto, Zita Vale, M Tiago, Isabel Sousa, Gabriel Praça, Hugo Santos, Morais","year":"2014","venue":"Integr. Comput. Eng","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Pinto et al., 2015] Tiago Pinto, Zita Vale, Tiago M. Sousa, and Isabel Praça (2015). Negotiation context analysis in electricity markets. Energy,. 85, 78-93","title":"Negotiation context analysis in electricity markets","authors":"Tiago Pinto, Zita Vale, M Tiago, Isabel Sousa, Praça","year":"2015","venue":"Energy","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ringler et al., 2016] Philipp Ringler, Dogan Keles, and Wolf Fichtner (2016). Agent-based modelling and simu- lation of smart electricity grids and markets -A literature review. Renew. Sustain. Energy Rev., 57, 205-215","title":"Agent-based modelling and simulation of smart electricity grids and markets -A literature review","authors":"Philipp Ringler, Dogan Keles, Wolf Fichtner","year":"2016","venue":"Renew. Sustain. Energy Rev","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"Santos et al. 2016]","title":null,"authors":"Santos","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Gabriel Santos, Tiago Pinto, Isabel Pra- ça, and Zita Vale (2016). MASCEM: Optimizing the performance of a multi-agent system. Energy, 111, 513- 524","title":"MASCEM: Optimizing the performance of a multi-agent system","authors":"Gabriel Santos, Tiago Pinto, Isabel Praça, Zita Vale","year":"2016","venue":"Energy","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Silva et al., 2016] Francisco Silva, Brigida Teixeira, Tiago Pinto, Gabriel Santos, Zita Vale, and Isabel Praça (2016). Generation of realistic scenarios for multi-agent simulation of electricity markets. Energy, 116, 128-139","title":"Generation of realistic scenarios for multi-agent simulation of electricity markets","authors":"Silva","year":"2016","venue":"Energy","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}