- \n
- Data Complexity:<\/strong> Energy forecasting is challenged by the stochastic nature of weather and human behavior, non-stationary consumption patterns, and limited data from lower-quality sensors.<\/li>\n\n\n\n
- Business Impact:<\/strong> Prediction errors lead to imbalance penalties, incorrect market bids, and energy waste.<\/li>\n\n\n\n
- Multi-Layer Solution:<\/strong> The proposed architecture includes IoT data acquisition, storage in time-series databases, simulation of system behavior using physical or AI\/ML models, and direct integration with an Energy Management System (EMS) for automated control.<\/li>\n<\/ul>\n\n\n
\n![\"\"](\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/distributed-energy-forecasting-1024x503.png\")
<\/figure>\n<\/div>\n\n\nRepresentation-Augmented Temporal Fusion Transformer (TFT) Architecture<\/strong><\/h3>\n\n\n\n
This hybrid approach learns stabilized temporal representations that are conditioned on structural information, providing interpretable forecasting<\/sup>.<\/p>\n\n\n\n
- \n
- Temporal Pattern Extraction:<\/strong> Uses a self-supervised encoder (TS2Vec) to transform raw energy signals into coherent representations, mitigating noise and data sparsity.<\/li>\n\n\n\n
- Structural Context Conditioning:<\/strong> Asset-specific structural information (text-based) is processed using a BERT-type encoder and injected into the temporal representations to capture asset-specific dynamics.<\/li>\n\n\n\n
- Interpretable Forecasting:<\/strong> The model dynamically selects relevant covariates at each step through a variable selection network and uses an \u201cInterpretable Masked Self-Attention\u201d mechanism to provide interpretability for its decisions.<\/li>\n<\/ul>\n\n\n
\n![\"\"](\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/distributed-energy-TFT.png\")
<\/figure>\n<\/div>\n\n\nConcept of \u201cAgentic Digital Twin\u201d<\/strong><\/h3>\n\n\n\n
It represents a next-generation model that integrates foundation models to bring proactivity, autonomy, and multimodal situational awareness into energy management systems<\/sup><\/sup><\/sup><\/sup><\/sup><\/sup><\/sup><\/sup><\/sup>.<\/p>\n\n\n\n
- \n
- Perception and Analytical Processing:<\/strong> Uses Retrieval-Augmented Generation (RAG) for data generation and extracts features from both structured and unstructured data.<\/li>\n\n\n\n
- Knowledge Bases:<\/strong> The system integrates dynamic knowledge (real-time data, logs), static knowledge (models of physical components), and policies (rules, constraints).<\/li>\n\n\n\n
- Action Planning and Reasoning:<\/strong> Reflective agents communicate with each other (inter-agent conversation) and generate scenarios. Symbolic reasoning is used to avoid system hallucinations and to implement optimizations, with safety mechanisms such as \u201cHuman-in-the-Loop\u201d.<\/li>\n<\/ul>\n\n\n
\n![\"\"](\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/agentic-digital-twin-1024x618.png\")
<\/figure>\n<\/div>","protected":false},"excerpt":{"rendered":"The research focuses on identifying AI-based solutions and Digital Twin concepts to address complex forecasting challenges in distributed energy networks (Smart Grids). Forecasting challenges and Digital Twin architecture. Representation-augmented Temporal Fusion Transformer (TFT) architecture. This hybrid approach learns stabilized temporal representations conditioned on structural information, providing a... <\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"default","neve_meta_enable_content_width":"on","neve_meta_content_width":100,"neve_meta_title_alignment":"","neve_meta_author_avatar":"off","neve_post_elements_order":"[\"content\"]","neve_meta_disable_header":"off","neve_meta_disable_footer":"","neve_meta_disable_title":"","_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1835","post","type-post","status-publish","format-standard","hentry","category-rezultate"],"_links":{"self":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1835","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/comments?post=1835"}],"version-history":[{"count":5,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1835\/revisions"}],"predecessor-version":[{"id":1873,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1835\/revisions\/1873"}],"wp:attachment":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/media?parent=1835"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/categories?post=1835"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/tags?post=1835"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Knowledge Bases:<\/strong> The system integrates dynamic knowledge (real-time data, logs), static knowledge (models of physical components), and policies (rules, constraints).<\/li>\n\n\n\n
- Structural Context Conditioning:<\/strong> Asset-specific structural information (text-based) is processed using a BERT-type encoder and injected into the temporal representations to capture asset-specific dynamics.<\/li>\n\n\n\n
- Business Impact:<\/strong> Prediction errors lead to imbalance penalties, incorrect market bids, and energy waste.<\/li>\n\n\n\n