Problem
Research questions and friction points this paper is trying to address.
electricity load forecasting
weather forecast aggregation
renewable energy integration
Combining Weather Forecast Aggregation and State-Space Models for Adaptive Probabilistic Electricity Load Forecasting
🤖 AI Summary
本文提出一种结合多气象预报和状态空间模型的自适应概率电力负荷预测框架，以减少天气预测误差的影响，并通过GAMLSS增强不确定性量化。
📝 Abstract
Accurate electricity load forecasting is essential to ensure the real-time balance between supply and demand, especially in systems increasingly influenced by weather conditions and renewable energy integration. In this paper, we propose an adaptive probabilistic forecasting framework that leverages multiple meteorological forecast providers to mitigate the impact of weather prediction errors on load forecasts. Our approach combines an adaptive generalized additive model (GAM) with a state-space formulation estimated via the Viking algorithm, enabling dynamic parameter updates over time. To improve uncertainty quantification, we extend the framework with a probabilistic layer based on GAM for location, scale and shape (GAMLSS), allowing the residual variance to depend on explanatory variables. We further introduce a short-term correction mechanism to enhance performance across different forecast horizons. A key contribution of this work is the aggregation of multiple meteorological inputs, whose diversity is shown to improve both point and probabilistic forecasts, particularly in capturing extreme quantiles by incorporating forecast dispersion. The methodology is evaluated on French national electricity consumption data and demonstrates consistent gains over single-provider approaches, with significant improvements in probabilistic forecasting accuracy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
adaptive probabilistic forecasting
generalized additive model (GAM)
state-space model
forecast aggregation
uncertainty quantification
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