Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting
Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting
复制标题
应用模糊神经网络基于长期预测来模拟天气参数变化对电力负荷的影响
DOI：
10.1016/j.aej.2016.12.008
复制
发表时间：
2017
影响因子：
6.8
通讯作者：
Musa Bulus Garkida
中科院分区：
文献类型：
--
作者：
Danladi Ali;Michael Yohanna;Puwu Markus Ijasini;Musa Bulus Garkida
长期负荷预测提供了有关未来负荷的重要信息，有助于电力行业做出有关电力生产和输送的决策。在这项工作中，建立了模糊神经网络模型来预测阿达马瓦州Mubi未来一年的负荷与天气参数(温度和湿度)的关系。观察到：电负荷随温度和相对湿度的升高而增加，对电负荷的影响不明显。预测精度为98.78%，相应的平均绝对百分比误差为1.22%。这证实了模糊神经网络是一种很好的负荷预测工具。
Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy – neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity) in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE) of 1.22%. This confirms that fuzzy – neuro is a good tool for load forecasting.