Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.

中文翻译：

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使用增强的进化前馈神经网络在约旦进行实时电力负荷预测

电力系统规划和扩展从预测预期的未来负载需求开始。从工程角度和财务角度来看，负荷预测是必不可少的。它在传统的垄断运营和电力公司规划中有效地发挥了至关重要的作用，以提高电力系统的运行、安全性、稳定性、运行成本的最小化和零排放。这里讨论了两个成熟的案例，以量化附加模型、观察、分辨率、数据类型的好处，以及数据对于约旦电力负荷预测的感知和演变的必要性。一年多的实际负荷数据是从约旦领先的电力公司获得的。这些案例基于每日总需求量和每小时每日需求量。这项工作的主要目的是根据 Jordan 的当前负载测量，轻松准确地计算一周前电力系统负载预测。预测中的不确定性可能会浪费金钱和资源。这项研究提出了一种使用最近的灰狼优化器 (GWO) 的优化多层前馈神经网络。功率预测问题被表述为一个最小化问题。实验结果与流行的优化方法进行了比较，表明所提出的方法提供了非常有竞争力的预测结果。这项研究提出了一种使用最近的灰狼优化器 (GWO) 的优化多层前馈神经网络。功率预测问题被表述为一个最小化问题。实验结果与流行的优化方法进行了比较，表明所提出的方法提供了非常有竞争力的预测结果。这项研究提出了一种使用最近的灰狼优化器 (GWO) 的优化多层前馈神经网络。功率预测问题被表述为一个最小化问题。实验结果与流行的优化方法进行了比较，表明所提出的方法提供了非常有竞争力的预测结果。