Browsing Publikasjoner fra CRIStin - SINTEF AS by Author "Najafi, Behzad"
Now showing items 1-4 of 4
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Assessing the impact of employing machine learning-based baseline load prediction pipelines with sliding-window training scheme on offered flexibility estimation for different building categories
Campodonico Avendano, Italo Aldo; Javan, Farzad Dadras; Najafi, Behzad; Moazami, Amin Nitter; Rinaldi, Fabio (Peer reviewed; Journal article, 2023)The present study is focused on assessing the impact of the performance of baseline load prediction pipelines on the estimation (by the grid operator) accuracy of the flexibility offered by different categories of buildings. ... -
Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization
Raymand, Farhang; Najafi, Behzad; Mamaghani, Alireza Haghighat; Moazami, Amin Nitter; Rinaldi, Fabio (Peer reviewed; Journal article, 2023)Smart meter-driven remote auditing of buildings, as an alternative to the labor-intensive on-site visits, permits large-scale and rapid identification of buildings with low energy performance. The existing literature has ... -
Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses
Javan, Farzad Dadras; Avendano, Italo Aldo Campodonico; Najafi, Behzad; Moazami, Amin Nitter; Rinaldi, Fabio (Peer reviewed; Journal article, 2023)This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling ... -
A novel framework for assessing the smartness and the smart readiness level in highly electrified non-residential buildings: A Norwegian case study
Campodonico Avendano, Italo Aldo; Andersen, Kamilla Heimar; Erba, Silvia; Moazami, Amin; Aghaei, Mohammadreza; Najafi, Behzad (Peer reviewed; Journal article, 2024)This work presents a new operational framework to measure the smartness and smart readiness of highly electrified buildings. The framework seeks to enhance legacy systems and controls of existing buildings and establish ...