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Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices

Toubeau, J. F., Morstyn, T., Bottieau, J., Zheng, K., Apostolopoulou, D. ORCID: 0000-0002-9012-9910, De Greve, Z., Wang, Y. and Vallee, F. C. (2020). Capturing Spatio-Temporal Dependencies in the Probabilistic Forecasting of Distribution Locational Marginal Prices. IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.3047863

Abstract

IEEE This paper presents a new spatio-temporal framework for the day-ahead probabilistic forecasting of Distribution Locational Marginal Prices (DLMPs). The approach relies on a recurrent neural network, whose architecture is enriched by introducing a deep bidirectional variant designed to capture the complex time dynamics in multi-step forecasts. In order to account for nodal price differentiation (arising from grid constraints) within a procedure that is scalable to large distribution systems, nodal DLMPs are predicted individually by a single model guided by a generic representation of the grid. This strategy offers the additional benefit to enable cold-start forecasting for new nodes with no history. Indeed, in case of topological changes, e.g. building of a new home or installation of photovoltaic panels, the forecaster intrinsically leverages the statistical information learned from neighbouring nodes to predict the new DLMP, without needing any modification of the tool. The approach is evaluated, along with several other methods, on a radial low voltage network. Outcomes highlight that relying on a compact model is a key component to boost its generalization capabilities in high-dimensionality, while indicating that the proposed tool is effective for both temporal and spatial learning.

Publication Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Electricity price forecasting, Deep learning, Bidirectional Long Short-Term Memory, Space-time correlation, Multistep-ahead time series forecasting.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date Deposited: 05 Feb 2021 14:59
URI: https://openaccess.city.ac.uk/id/eprint/25564
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