Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data
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Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Probabilistic load forecasting (PLF) has been extensively studied to characterize the uncertainties of future loads. Traditional PLF is implemented based on the historical load data itself and other relevant factors. However, the prevalence of smart meters provides more fine-grained consumption information. This paper proposes a novel probabilistic aggregated load forecasting algorithm that makes full use of fine-grained smart meter data. It first applies clustering-based methods for point aggregated load forecasting. By varying clustering algorithms, multiple point forecasts can be obtained. On this basis, different quantile regression models are implemented to combine these point forecasts in order to form the final probabilistic forecasts. Case studies on a real-world dataset demonstrate the superiority of our proposed method.
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Publication status
published
Editor
Book title
2021 IEEE Madrid PowerTech
Journal / series
Volume
Pages / Article No.
9494815
Publisher
IEEE
Event
14th IEEE PowerTech Conference (PowerTech 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Probabilistic load forecasting; smart meter; load aggregation; Quantile regression
Organisational unit
09481 - Hug, Gabriela / Hug, Gabriela
Notes
Conference lecture held on June 29, 2021