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首页> 外文期刊>IOSR journal of electrical and electronics engineering >Short-Term (Seven Day Basis) Load Forecasting Of a Grid System in Bangladesh Using Artificial Neural Network
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Short-Term (Seven Day Basis) Load Forecasting Of a Grid System in Bangladesh Using Artificial Neural Network

机译：使用人工神经网络的短期（七天基础）孟加拉国网格系统的负载预测

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摘要

Load forecasting is the technique for prediction of electrical load. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfil the demand, there would be Problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. This paper proposes the load forecasting for the Power Grid Company Bangladesh Ltd. (PGCB) by using Artificial Neural Network(ANN). It uses the advanced back propagation algorithm and the data from PGCB to train the system. This thesis has proposed to train the network in summer and winter to minimize the power as well as the cost of generation. It consists with the daily data of all seasons and the data of Friday, public holidays, Eid festival and Durga puja which represent the holiday. The input pattern is considered from the load variation events. And only that kind of inputs are chosen for which the shows a great performance by providing the output nearer to the actual value. The network is implemented by MATLAB programming language and then the results are compared and analyzed in terms of accuracy. For this thesis, more variables are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.
机译：负载预测是用于预测电负载的技术。在一个解除管制的市场中，一家发电公司需要了解靠近准确功率的市场负荷需求。如果生成不足以满足需求，则会出现不规则的供应问题，如果多次发电，则发电公司将不得不承担损失。最近已经建议通过大量研究人员进行短期负荷预测的神经网络技术。本文提出了使用人工神经网络（ANN）的电网公司孟加拉国公司的负荷预测（PGCB）。它使用高级后传播算法和来自PGCB的数据来训练系统。本文提议在夏季和冬季培训网络，以最大限度地减少电力以及生成成本。它包括所有季节的日常数据以及星期五，公众假期，Eid节和代表假期的Durga Puja的数据。从负载变化事件考虑输入模式。只有选择这种输入，通过提供更靠近实际值的输出来表示良好的性能。该网络由MATLAB编程语言实现，然后在准确性方面进行比较和分析结果。对于本文，在神经网络模型中使用更多变量以实现更高的短期负荷预测结果的更准确度。

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