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Long-term Electrical load forecasting based on economic and demographic data for Turkey

机译:基于经济和人口数据的土耳其长期电力负荷预测

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Load forecasting is very important to operate the electric power systems. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Long term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption data, national incoming, urbanization rate, population increasing rate and as well as other economic parameters. Artificial Neural Network (ANN) and Artificial Neural Fuzzy Inference System (ANFIS) are the famous artificial intelligence methods and have widely used to solve forecasting problems in literature. In this study, artificial intelligence methods and mathematical modeling (MM) are used to forecast long term energy consumption and peak load for Turkey. The four different input data are used to obtain two different outputs in all three methods. Using the four different variables especially in mathematical modeling has been a novelty for Turkey case study. The results obtained from ANFIS, ANN and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used.
机译:负荷预测对于操作电力系统非常重要。电力公司的主要任务之一是始终准确地预测负载需求,尤其是长期需求。需要长期负荷预测(LTLF)计划并进行未来的能源需求和投资,例如发电厂的规模。 LTLF受能源消耗数据,国民收入,城市化率,人口增长率和其他经济参数的影响。人工神经网络(ANN)和人工神经模糊推理系统(ANFIS)是著名的人工智能方法,已广泛用于解决文献中的预测问题。在这项研究中,人工智能方法和数学建模(MM)用于预测土耳其的长期能源消耗和峰值负荷。在这三种方法中,四种不同的输入数据用于获得两种不同的输出。在土耳其的案例研究中,尤其是在数学建模中使用这四个不同的变量已是一种新颖的做法。比较从ANFIS,ANN和MM获得的结果以显示可用性。为了显示误差级别,使用了平均绝对百分比误差(MAPE)和平均绝对误差(MAE)。

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