Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach

TL;DR

This paper introduces a meta-learning approach for few-shot load forecasting in smart grids, enabling rapid adaptation and accurate predictions with minimal data, outperforming existing methods.

Contribution

The paper adapts a model-agnostic meta-learning algorithm for short-term load forecasting, demonstrating superior performance with limited data and proposing a new evaluation metric.

Findings

Achieves 12.5% better accuracy than transfer learning.

Performs robustly across different hyperparameters and series lengths.

Introduces mean average log percentage error metric.

Abstract

Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established model-agnostic meta-learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series' short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by $12.5\%$. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model's robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models.