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Mid-term load forecasting of power systems by a new prediction method

机译：一种新的预测方法对电力系统的中期负荷进行预测

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Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and non-stationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the European Network on Intelligent TEchnologies (EUNITE) test data and Iran's power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem.
机译：中期负荷预测（MTLF）成为当今电力系统的重要工具，主要是在电力系统运行在解除管制的国家中。在不同种类的MTLF中，本文着重于预测未来一个月的每日峰值负荷。这种负荷预测具有许多应用程序，例如维护计划，中期水热协调，充足性评估，有限能源单位的管理，远期合同的谈判以及开发具有成本效益的燃料购买策略。但是，每日峰值负载是非线性，易失且不稳定的信号。此外，缺乏足够的数据通常会使这个问题更加复杂。本文提出了一种新的解决方法，该方法由高效的数据模型，预测机制以及神经网络和进化算法的组合作为混合预测技术组成。欧洲智能技术网络（EUNITE）测试数据和伊朗的电力系统对提出的方法进行了审查。我们还将我们的策略与其他MTLF方法进行比较，以揭示其解决此负荷预测问题的能力。

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