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PV Power Production...
PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids
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- Aupke, Phil (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Theocharis, Andreas (författare)
- Karlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)
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- Kassler, Andreas, 1968- (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Archer, Dan-Eric (författare)
- CheckWatt AB, Sweden
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(creator_code:org_t)
- IEEE, 2023
- 2023
- Engelska.
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Ingår i: 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). - : IEEE. - 9798350347432 - 9798350347449
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- For efficient energy exchanges in smart energy grids under the presence of renewables, predictions of energy production and consumption are required. For robust energy scheduling, prediction of uncertainty bounds of Photovoltaic (PV) power production and consumption is essential. In this paper, we apply several Machine Learning (ML) models that can predict the power generation of PV and consumption of households in a smart energy grid, while also assessing the uncertainty of their predictions by providing quantile values as uncertainty bounds. We evaluate our algorithms on a dataset from Swedish households having PV installations and battery storage. Our findings reveal that a Mean Absolute Error (MAE) of 16.12W for power production and 16.34W for consumption for a residential installation can be achieved with uncertainty bounds having quantile loss values below 5W. Furthermore, we show that the accuracy of the ML models can be affected by the characteristics of the household being studied. Different households may have different data distributions, which can cause prediction models to perform poorly when applied to untrained households. However, our study found that models built directly for individual homes, even when trained with smaller datasets, offer the best outcomes. This suggests that the development of personalized ML models may be a promising avenue for improving the accuracy of predictions in the future.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Energisystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Energy Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- Machine Learning
- Smart Energy Grids
- Uncertainty Bounds
- Digital storage
- Forecasting
- Smart power grids
- Uncertainty analysis
- Energy exchanges
- Energy grids
- Machine learning models
- Machine-learning
- Photovoltaic power
- Power production
- Production and consumption
- Smart energies
- Smart energy grid
- Elektroteknik
- Electrical Engineering
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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