{"id":2567,"date":"2022-09-27T18:39:00","date_gmt":"2022-09-27T16:39:00","guid":{"rendered":"https:\/\/p688098.mittwaldserver.info\/project\/predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models\/"},"modified":"2025-03-28T19:34:37","modified_gmt":"2025-03-28T17:34:37","slug":"predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models","status":"publish","type":"project","link":"https:\/\/www.aee-intec.at\/en\/project\/predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models\/","title":{"rendered":"PredictIT2.0 – Software for operation optimization in district heating networks based on self-learning load forecast models"},"content":{"rendered":"\n

The majority of Austrian district heating network operators are SMEs. In this field, innovative digital methods for increasing efficiency are rarely or never used in practice. The project PredictIT2.0 aims at the development of a low-threshold access to digital operating point optimization based on self-learning load forecast models in district heating networks. The aim is to develop a software package that is characterized by a modular structure and can be coupled with existing control systems via general interfaces. By using local weather forecast data as well as heat network operation data in the software, high temporal resolution forecasts for the heat demand can be generated for individual consumers as well as for the entire network. This should enable SMEs to operate heat generation and heat storage more efficiently in terms of costs and emissions. The participating ACR institutes can enhance and expand their individual service portfolios by reusing individual software modules of PredictIT2.0. <\/p>\n","protected":false},"author":4,"featured_media":2566,"parent":0,"menu_order":0,"template":"","class_list":["post-2567","project","type-project","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nPredictIT2.0 - Software for operation optimization in district heating networks based on self-learning load forecast models - AEE INTEC<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.aee-intec.at\/en\/project\/predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models\/\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.aee-intec.at\\\/en\\\/project\\\/predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models\\\/\",\"url\":\"https:\\\/\\\/www.aee-intec.at\\\/en\\\/project\\\/predictit2-0-software-for-operation-optimization-in-district-heating-networks-based-on-self-learning-load-forecast-models\\\/\",\"name\":\"PredictIT2.0 - 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