Application of a Hybrid Machine Learning model on short term electricty demand prediction

Assar, Ahmed Khaled Ahmed Farouk
Electricity demand forecasting is an important procedure in the electricity market and plays a great role in assuring a sustainable and efficient operation chain. By accurately forecasting the demand, one can see a considerable reduction in production costs as well as saving energy resources. Therefore, optimizing the demand forecasting techniques became an inseparable goal of power economics, leading to the introduction of machine learning to this sector that proved to be superior to other pre-defined alternatives. This thesis proposes to apply a Hybrid model that combines two forecasting machine learning algorithms; namely Support Vector Regression (SVR) and Long Short-Term Memory (LSTM). The hourly data from the Spanish electricity market is used to forecast the day-ahead electricity consumption in the last quarter of the year 2018, with weather Variables being fed to the models as the inputs. The performance of the proposed model is compared with the Temperature regression and load projection model (the actual model used in Spain), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs), and SVR and LSTM separately. The combined method's forecasting results were shown to be superior to all four suggested independent approaches, and it was able to successfully minimize errors and enhance the accuracy between actual and forecasted values. However, the proposed Hybrid model didn’t outperform the already applied approach and achieved a Mean Absolute Percentage Error (MAPE) of 1.71557 and a Mean Absolute Error (MAE) of 488.269.


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Citation Formats
A. K. A. F. Assar, “Application of a Hybrid Machine Learning model on short term electricty demand prediction,” M.S. - Master of Science, Middle East Technical University, 2022.