Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of decisions that generally has a direct non-strict relationship with the decisions quality. This is the most important reason that why the endeavor for enhancement the forecasting accuracy has never been stopped in the literature. Electricity load forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of the electricity load is essential for precise planning and implementation of the necessary policies. For this reason electricity load forecasting is important for financial and, operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult for forecasting in comparison with other commodities is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets. Computational intelligence and soft computing approaches are among the most precise and useful approaches for modeling the complexity and uncertainty in data, respectively. In the literature, several hybrid models have been developed in order to simultaneously use unique advantages of these models. However, iterative suboptimal meta-heuristic based models are always used for combining in these models. In this paper, a direct optimum parallel hybrid (DOPH) model is proposed based on multilayer perceptrons (MLP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to electricity load forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems in a direct and optimal structure. It can be theoretically demonstrated that the proposed model due to use the direct optimal structure, can achieve non-less accuracy than iterative suboptimal hybrid models, while its computational costs are significantly lower than those hybrid models. Empirical results indicate that the proposed model can achieve more accurate results rather than its component and some other seasonal hybrid models. (C) 2017 Elsevier Ltd. All rights reserved.