  Load forecasting is a crucial topic in energy management systems (EMS) due to
its vital role in optimizing energy scheduling and enabling more flexible and
intelligent power grid systems. As a result, these systems allow power utility
companies to respond promptly to demands in the electricity market. Deep
learning (DL) models have been commonly employed in load forecasting problems
supported by adaptation mechanisms to cope with the changing pattern of
consumption by customers, known as concept drift. A drift magnitude threshold
should be defined to design change detection methods to identify drifts. While
the drift magnitude in load forecasting problems can vary significantly over
time, existing literature often assumes a fixed drift magnitude threshold,
which should be dynamically adjusted rather than fixed during system evolution.
To address this gap, in this paper, we propose a dynamic drift-adaptive Long
Short-Term Memory (DA-LSTM) framework that can improve the performance of load
forecasting models without requiring a drift threshold setting. We integrate
several strategies into the framework based on active and passive adaptation
approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze
the proposed framework and deploy it in a real-world problem through a
cloud-based environment. Efficiency is evaluated in terms of the prediction
performance of each approach and computational cost. The experiments show
performance improvements on multiple evaluation metrics achieved by our
framework compared to baseline methods from the literature. Finally, we present
a trade-off analysis between prediction performance and computational costs.
