Electrical Load Forecasting Model Based on Long-Short Term Memory in Day-Ahead Electricity Markets


Duc Nguyen Huu*, Dinh Van Thin


Electric Power University, Hanoi, Vietnam

Corresponding Author Email: ducnh@epu.edu.vn

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Electrical load forecasting plays an important role for participants in electricity markets. In electricity markets such as the day-ahead market and the intraday market, it is required to forecast load demand 24 hours in advance for participating in the day-ahead electricity market. Thus, the problem of predicting the electricity load 24 hours in advance plays a vital role in participating into the day-ahead market. Due to the non-linearity and instability under natural conditions of electrical loads in small-scale power systems, accurate forecasting is still a challenge. This paper introduces an estimation model of the electrical load using of Long-Short-Term-Memory (LSTM) short-term memory structure based on feedback neural network structure to predict electrical loads 24 hours in advance.