{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:06:06Z","timestamp":1759133166964},"reference-count":15,"publisher":"Ediciones Universidad de Salamanca","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ADCAIJ"],"abstract":"<jats:p>Artificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast \/ estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices\u2019 similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts\u2019 history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.<\/jats:p>","DOI":"10.14201\/adcaij2016512336","type":"journal-article","created":{"date-parts":[[2016,10,26]],"date-time":"2016-10-26T14:43:50Z","timestamp":1477493030000},"page":"23-35","source":"Crossref","is-referenced-by-count":15,"title":["Dynamic Fuzzy Clustering Method for  Decision Support in Electricity  Markets Negotiation"],"prefix":"10.14201","volume":"5","author":[{"given":"Ricardo","family":"Faia","sequence":"first","affiliation":[]},{"given":"Tiago","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Zita","family":"Vale","sequence":"additional","affiliation":[]}],"member":"5351","published-online":{"date-parts":[[2016,7,7]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","DOI":"10.14201\/adcaij2015415464"},{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-19033-4_23","volume-title":"Swarm-Based Smart City Platform","author":"Chamoso","year":"2015","unstructured":"Chamoso, P., De La Prieta, F., 2015. 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In: 5th Symposium on Mathematical Statistics and Probability. pp. 281\u2013297."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/BF02478259"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tej.2005.06.008"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"399","DOI":"10.3233\/ICA-140477","article-title":"Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation.","volume":"21","author":"Pinto","year":"2014","unstructured":"Pinto, T., Vale, Z., Sousa, T.M., Pra\u00e7a, I., Santos, G., Morais, H., 2014. Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation. Integr. Comput. 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