It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variables in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.

显然，开发更准确的预测方法是构建健壮的多能量系统（MES）的基础。在这种情况下，对于具有强大的现代电力系统扩展计划计划的长期预测也是必不可少的。尽管通常用点估计来表示非常短期和短期的预测，但是由于长期的天气变量等预测变量具有内在的不确定性，因此这种方法在中期和长期的预测中非常不可靠。因此，长期预测通常由基于概率函数的概率预测值表示。在本文中，考虑到季节性相似性，人口，国内生产总值（GDP）和电价的最重要驱动因素，开发了自组织混合网络（SOMN）来估计长期范围内峰值负荷的概率密度函数（PDF）。所提出的方法用于预测澳大利亚昆士兰州的年度和季节性高峰负荷的PDF。