Predicting 24-hours ahead photovoltaic power output using forecast information

Kim, Jae Hyun; Kim, Taehyoung; Lee, Jieun; Ham, Kyung Sun

doi:10.1145/3297280.3297593

Cited by 5 publications

(1 citation statement)

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“…In research, related work on anticipating energy-driven disruptions in process industry focuses on linear programming models for estimation of energy disruptions from earth quakes (Janev et al, 2021), organizational learning approaches for adaption to climate changes (Orsato et al, 2017) as well as knowledge management in energy data spaces (Rose et al, 1997). In addition to that, there is related work focusing on the energy industry itself, considering probabilistic risk assessment for preventing safety related disruptions (Blanco et al, 2019;Kosai and Unesaki, 2017), anticipation of power generation and outages (Kim et al, 2019;Moghavvemi and Faruque, 1999) as well as outage management approaches (He et al, 2016). Objective of our research is the anticipation of such energy-driven crises in process industry by AI-based scenario planning for improving resilience in manufacturing.…”

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confidence: 99%

The ‘Why’ Behind the Lie: Towards a Better Understanding of Health Information Disclosure in the Patient-Provider Interaction

Clark,

Keith

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results.

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