Hybrid Techniques for Short Term Load Forecasting

Majlesi Journal of Electrical Engineering

دوره 17، شماره 1، خرداد 2023، صفحه 55-61   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1970200.0

نویسندگان

Saroj Panda¹؛ Papia Ray^{* 1}؛ Surender Salkuti²

¹Veer Surendra Sai Univetsity of Technology, Burla, India

²Woosong University, Daejon, Republic of Korea

چکیده

Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search  (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable.

کلیدواژه‌ها

Short Term Load Forecasting؛ Hybrid Techniques؛ Artificial Neural Network؛ Back Propagation؛ Cuckoo Search؛ Bat Algorithm

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