An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads

Kofi Afrifa Agyeman, Gyeonggak Kim, Hoonyeon Jo, Seunghyeon Park and Sekyung Han
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Kofi Afrifa Agyeman: Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea
Gyeonggak Kim: Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea
Hoonyeon Jo: Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea
Seunghyeon Park: Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea
Sekyung Han: Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea

Energies, 2020, vol. 13, issue 10, 1-20

Abstract: 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.

Keywords: Bayesian; deep neural network; demand load forecast; distributed load; ensemble algorithm stochastic; K-means (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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