Improving load forecasting based on deep learning and K-shape clustering

One of the most crucial tasks for utility companies is load forecasting in order to plan future demand for generation capacity and infrastructure. Improving load forecasting accuracy over a short period is a challenging open problem due to the variety of factors that influence the load, and the volume of data that needs to be considered. This paper proposes a new approach for short term load forecasting using an effective new combination of clustering and deep learning methods, along with a new weighted aggregation mechanism. Our evaluation using smart meter data from a publicly available real-life dataset demonstrates the improved accuracy of our approach over existing methods.