An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads

• Main Authors: Kofi Afrifa Agyeman, Gyeonggak Kim, Hoonyeon Jo, Seunghyeon Park, Sekyung Han
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Energies
• Subjects: Bayesian; deep neural network; demand load forecast; distributed load; ensemble algorithm stochastic; K-means
• Online Access: https://www.mdpi.com/1996-1073/13/10/2658

Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven stochastic ensemble model framework for short-term and long-term demand load forecasts. Our proposed framework reduces uncertainties in the load forecast by fusing homogenous models that capture the dynamics in load state characteristics and exploit model diversities for accurate prediction. The ensemble model caters for factors such as meteorological and exogenous variables that affect load prediction accuracy with adaptable, scalable algorithms that consider weather conditions, load features, and state characteristics of the load. We defined a heuristic trained combiner model and an error correction model to estimate the contributions and compensate for forecast errors of each prediction model, respectively. Acquired data from the Korean Electric Power Company (KEPCO), and building data from the Korea Research Institute, together with testbed datasets, were used to evaluate the developed framework. The results obtained prove the efficacy of the proposed model for demand load forecasting.