Long Short Term Memory combined with Genetic Algorithm to Predict Short-Term Load Forecasting
| Autor: | ARPITA SAMANTA SANTRA, 莎曼塔 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 108 Electricity load forecasting is an important task to enhance energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists optimizing the resources and minimizing the energy wastage. The main motivation of this study is to improve the robustness of short-term load forecasting (STLF) by utilizing long short term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method is trained using actual load and weather data, and the performance results show that it yields small mean absolute percentage error on the test data. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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