Short-Term Load Forecasting of Multiregion Systems Using Mixed Effects Models

This paper presents an application of linear mixed models to short-term load forecasting. The starting point of the design is a currently working model at the Spanish Transport System Operator, which is based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. The integration of several regions into a linear mixed model allows using the information from other regions to learn general behaviors present in all regions while also identifying individual deviation in each regions. This technique is especially useful when modeling the effect of special days for which information from the past is scarce. The model described has been applied to the three most relevant regions of the system, focusing on special day and improving the performance of both currently working models used as benchmark.