TY - GEN
T1 - PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids
AU - Aupke, Phil
AU - Seema,
AU - Theocharis, Andreas
AU - Kassler, Andreas
AU - Archer, Dan Eric
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Smart Energy Grids
KW - Uncertainty Bounds
UR - http://www.scopus.com/inward/record.url?scp=85168697748&partnerID=8YFLogxK
U2 - 10.1109/EEEIC/ICPSEurope57605.2023.10194894
DO - 10.1109/EEEIC/ICPSEurope57605.2023.10194894
M3 - Conference contribution
AN - SCOPUS:85168697748
T3 - Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
BT - Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
A2 - Leonowicz, Zbigniew
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023
Y2 - 6 June 2023 through 9 June 2023
ER -