  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.
