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: firstname.lastname@example.org
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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.