Abstract
Short-term load forecasting is a key task for planning and stability of the current and future distribution grid, as it can significantly contribute to the management of energy market for ancillary services. In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting problem on a substation level. In this context, sparse representation theory can provide parsimonial predictive models, which become attractive mainly due to their ability to successfully model the input space in a self-learning manner, by interacting between theory, algorithms, and applications. Several techniques are implemented, incorporating numerous dictionary learning and sparse decomposition algorithms, and a hierarchical structured model is proposed. The concept of sparsity in each case is embedded throughout the utilization of different regularization forms which include the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tree</sup> norms. The observed superiority of the proposed theory, especially the one which embeds the atoms and corresponding coefficients in a tree structure, stems from the construction of the dictionary so as to represent efficiently the ambient electricity signal space and the consequent extraction of sparse basis-vectors. The performance of each model is evaluated using real hourly load measurements from a high voltage/medium voltage (HV/MV) substation and compared with that of widely used machine learning methods. The provided analytical results, verify the effectiveness of hierarchical sparse representation in short-term load forecasting applications, in terms of common accuracy indices.
Highlights
The ever-increasing energy requirements of modern power grids provide, beyond any doubt, evidence of the necessity to manage energy resources in the utmost efficient and cost-effective way
We are permitting the simultaneous activation of specific coefficients that are part of a group, which will advance the dictionary elements to self-organize patterns in order to adapt the prior. This reinforces the exploitation of sparse-based models for short-term net load forecasting as highlighted by the results presented below, especially for those that utilize a structured type of sparse representation
The prediction of the net load with a time horizon of one hour is studied in this article, presenting a new method based on sparse representation
Summary
The ever-increasing energy requirements of modern power grids provide, beyond any doubt, evidence of the necessity to manage energy resources in the utmost efficient and cost-effective way. Electric load forecasting on a substation scale, plays central role in this effort, and this discipline, has become one of the major research fields in the context of electrical engineering [1]. The scope of this field is to provide predictions regarding the future values of load timeseries based on previous collections of load measurements, while often taking into account exogenous variables. As it is expected, a number of challenges appear when dealing.
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