{"id":"doi:10.1016/j.egyai.2024.100459","kind":"article","title":"Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model","authors":["Amir Miraki","Pekka Parviainen","Reza Arghandeh"],"author_count":3,"year":2024,"published":"2024-12-12","venue":"Energy and AI","cited_by":44,"doi":"10.1016/j.egyai.2024.100459","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.egyai.2024.100459","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.egyai.2024.100459","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4405309949","pdf_url":null,"abstract":"Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems , necessitating accurate forecasting. Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns. This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling. Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations , enabling improved forecasting accuracy. Through extensive evaluation on two real-world datasets focused on renewable energy and electricity demand, we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks. By explicitly modeling variable interdependencies and incorporating temporal information, our method provides reliable probabilistic forecasts, crucial for effective decision-making and resource allocation in the energy sector. Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score (CRPS) by 2.1%–70.9%, Mean Absolute Error (MAE) by 4.4%–52.2%, and Root Mean Squared Error (RMSE) by 7.9%–53.4% over existing methods on two real-world datasets.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Energy Load and Power Forecasting"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model\" by Amir Miraki, Pekka Parviainen, Reza Arghandeh, https://doi.org/10.1016/j.egyai.2024.100459, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"generative-multimodal","name":"Generative & Multimodal Models","domain_slug":"ai"}],"cited_in_dossiers":[]}