Aggregated Net-load Forecasting using Markov-Chain Monte-Carlo Regression and C-vine copula

Abstract

Net-load is the difference between total load and renewable generation and acts as an effective system load to which dispatchable generators are scheduled and system flexibility requirements are estimated. This necessitates accurate net-load forecasts for optimum scheduling and flexibility requirement estimations. Net-Load Forecasting (NLF) got only little attention in existing literature even though it is essential for optimal generation scheduling and power system flexibility requirement estimations. Time series and machine learning models have been used for NLF in recent years, however, there is vast scope existing for improving accuracy. Probabilistic forecasting models are widely used for various forecasting problems such as load, wind, and solar generation forecasting as those models show improved forecasting accuracy. In this context, this paper proposes a novel probabilistic aggregated very short-term NLF model based on Markov Chain Monte Carlo (MCMC) Regression. MCMC uses data augmentation, where unobserved variables are simulated from their posterior distribution and this makes MCMC approach suitable for regression. Further, MCMC forecasts are improved by incorporating C-vine copula-based Joint Probability Distribution (JPD) of expected load, wind, and solar generation forecasting errors. The C-vine copula is suitable for such multi-variable JPD estimation due to its enormous flexibility in stochastic multivariate dependence modelling compared to standard elliptical and Archimedean copulas. Results show that the proposed NLF model outperforms reference models and can produce accurate net-load forecasts.