Remodelling State-Space Prediction With Deep Neural Networks for Probabilistic Load Forecasting

Probabilistic load forecasting (PLF) has become necessary for power system operators to do efficient planning across power transmission and distribution systems. However, there are not many PLF models, and those that exist take a lot of computation time and are not efficient, especially in multiple loads. This paper proposes a novel algorithm for spatially correlated multiple loads wherein a global parameter is learned from state-space parameters of individual loads by an amalgamation of deep neural networks and state-space models. The proposed model employs complex pattern learning capabilities of recurrent neural networks and temporal pattern extraction of innovation state-space models. It is tested on GEFCom-14 and ISO-NE datasets, one with a single load and multiple loads. Different case studies are conducted to examine the involvement of temperature for load forecasting. It has been observed that in the case of multivariate loads, temperature variable doesn’t make much difference in PLF, but in the case of univariate loads, forecasting results are four-times better. The proposed method is highly interpretable and can be employed in areas where limited training data is available to the areas where colossal data is available. The proposed model has outperformed several benchmarks present in the literature on the same datasets.