Adaptive load forecasting using reinforcement learning with database technology

ABSTRACT Load forecasting is an essential operation in the power utility industry. However, a common challenge is faced for adjusting forecasting models to fit the need for substations’ load prediction as well as minimizing expenditure in IT resources for repurposing these forecasting models to bigger datasets. The goal of this paper is to propose a novel solution that is responsive to these demands through the integration of reinforcement learning with load forecasting on existing database technology. To deal with the varying accuracy of the forecasting models on different substations’ loads, the proposed solution compares and uses the best models and recalibrate them iteratively by comparing the model’s prediction against the actual load data. As shown in empirical analysis, the solution interacts with the environment and performs the optimum forecasting routine intuitively.