SHORT-TERM LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK TECHNIQUES: A CASE STUDY FOR REPUBLIC OF NORTH MACEDONIA

被引：0
|
作者：
Kotevska, Ana [1 ]
Rogleva, Nevenka Kiteva [1 ]
机构：
[1] Fac Elect Engn & Informat Technol, Skopje, North Macedonia
来源：
INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY | 2023年 / 15卷 / 03期
关键词：
Artificial Neural Network (ANN); Short Term Load Forecasting (STLF); Back Propagation; Mean Absolute Percentage Error (MAPE); SIMULATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Modernization and liberalization of power system in North Macedonia offers an opportunity to supervise and regulate the power consumption and power grid. This paper proposes models for short-term load forecasting using artificial neural network in order to balance the demand and load requirements and to determine electricity price. Neural network approach has the advantage of learning directly from the historical data. This method uses multiple data points. In this research, it can be confirmed that the quality of the short term prediction depends on the size of the data set and the data transformation.
引用
收藏
页码：97 / 106
页数：10
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