Article detail · 2021
Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting
- Year
- 2021
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue INTERNATIONAL JOURNAL OF GREEN ENERGY
- Catalog match (ISSN) International Journal of Green Energy
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Renewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country’s energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
47 citations
OpenAlex cited_by_count (cache / database)
28 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Artificial neural network approach for monthly air temperature estimations and maps 2023
- Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye 2023
- One-hour-ahead solar radiation forecasting by MLP, LSTM, and ANFIS approaches 2023
- One-hour-ahead solar radiation forecasting by MLP, LSTM, and ANFIS approaches 2023
- One-hour-ahead solar radiation forecasting by MLP, LSTM, and ANFIS approaches 2023
- Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches 2022
- Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches 2022
- Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches 2022
- The technical and economical feasibility study of offshore wind farms in Turkey 2023
- Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches 2022