A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving

For the last ten years, utilities have observed ongoing transitions on consumers' load curves. The previously flat load curves have more frequently turned into duck-shape. This is jointly caused by the increasing household loads as well as the popularity of rooftop solar photovoltaic. Such load...

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Veröffentlicht in:IEEE Power & Energy Society General Meeting S. 1 - 5
Hauptverfasser: Wang, Xinan, Wang, Yishen, Shi, Di, Wang, Jianhui
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2019
Schlagworte:
Accuracy
Autoencoders
Battery energy storage system
BESS
Costs
Demand side management
Forecasting
Machine learning
Neural networks
Peak shaving
Photovoltaic systems
Smart meters
solar photovoltaics
stacked autoencoder (SAE)
ISSN:1944-9933
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Beschreibung
Zusammenfassung:For the last ten years, utilities have observed ongoing transitions on consumers' load curves. The previously flat load curves have more frequently turned into duck-shape. This is jointly caused by the increasing household loads as well as the popularity of rooftop solar photovoltaic. Such load curve transition challenges the operation flexibility of the existing systems and greatly increases the per-MWh energy costs. Peak shaving, in this context, becomes a critical task to demand-side management. Owing to the development of Battery Energy Storage Systems (BESS), numerous peak shaving strategies have been developed and implemented. In this paper, by applying a stacked autoencoder (SAE)-based residential peak load curve forecasting technology, we further lift the peaking shaving capabilities of BESSs to a new level. The proposed strategy takes into account the welfares of both generation-side and demand-side and reaches an optimal balance. A comprehensive case study using smart meter data demonstrates the effectiveness of the proposed method in peak shaving application.
ISSN:1944-9933
DOI:10.1109/PESGM40551.2019.8973624