Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
This work addresses the problem of reliable and interpretable load forecasting for electric power system planning, with incremental improvements through domain-specific integration.
The paper tackles the challenge of accurate electric load forecasting under limited data and extreme conditions by proposing an interactive Generalized Additive Model (GAM) that incorporates domain knowledge, resulting in outperforming state-of-the-art methods on public benchmarks and electricity datasets with good generalization in extreme weather events.
Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste of energy. Accurate electric load forecasting is challenging when there is limited data or even no data, such as load forecasting in holiday, or under extreme weather conditions. As high-stakes decision-making usually follows after load forecasting, model interpretability is crucial for the adoption of forecasting models. In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. This boosting-based GAM leverages piecewise linear functions and can be learned through our efficient algorithm. In both public benchmark and electricity datasets, our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product, a unified AI platform for electricity forecasting.