Wind energy forecasting methods: A case study of the long short term memory model (LSTM)

This thesis synthesizes findings from three distinct research components, aiming to advance the field of wind energy prediction and promote sustainable energy management. The initial study explores wind energy prediction utilizing Long Short-Term Memory (LSTM) and other methodologies. The investigation focuses on forecasting power output based on wind speed data, addressing challenges related to missing values and seasonal patterns. Results from initial analyses, including ARIMA models and correlation assessments between wind speed and power output, revealed distinct peaks in power output during specific months, notably July, August, and September, corresponding with wind speed fluctuations. The study identified that wind speeds above 2.5 meters per second initiate power generation, peaking around 8 m/s, with graphical representations indicating a sigmoid relationship between wind speed and power output. Subsequently, alternative modeling approaches were explored after the failure of the SARIMA model. XG Boost, Random Forest Regressor, and LSTM were considered, with a detailed examination of the dataset's properties through visualization and statistical analysis. The prevalence of missing cells underscored the importance of meticulous data handling. The data overview and preprocessing phase detailed the process of data importation, recognition of the date column, handling of duplicate entries, and exploration through Pandas profiling and boxplots. Key variables such as "Active Power" and "Ambient Temperature" were discussed, along with the challenge of missing values and the identification of redundant variables. The final phase encapsulated the methodology used, emphasizing the importance of addressing missing data points and anomalies for accurate analysis. The rigorous cleaning process, model selection (SARIMA, XG Boost, Random Forest Regressor, LSTM), and their respective performance were discussed. Furthermore, the significance of data accuracy, the impact of wind speed on power output, and the necessity for varied modeling methods to capture wind energy dynamics effectively were highlighted. Building on these findings, several recommendations for advancing wind energy prediction and sustainable management were proposed. Advanced data pre-processing methods were suggested to enhance dataset quality and dependability, including the handling of missing values, outliers, and noise. Hybrid modeling technologies that combine classical statistical methodologies and machine learning algorithms were recommended for more accurate predictions. Incorporating meteorological and geographical elements into feature engineering methodologies was suggested to better understand power output. Developing more interpretable models to comprehend the relationship between relevant variables and wind energy generation was emphasized for informed decision-making. Ensemble learning methods, such as model averaging and stacking, were proposed to increase prediction accuracy by minimizing model flaws. The utilization of real-time data streams and advanced monitoring systems for dynamic weather patterns and environmental conditions was encouraged for adaptive forecasting models. A rigorous sensitivity study was suggested to assess forecasting model robustness to parameter adjustments, identifying the most relevant variables affecting wind energy generation. Ensuring the reliability and generalizability of forecasting models across different geographical locations and environmental conditions was emphasized through rigorous model validation and verification. Long-term wind energy generation forecasting studies were proposed to plan sustainable energy infrastructure in the face of changing climate dynamics and global energy demands. Finally, collaboration between academic institutions, industry stakeholders, and government agencies was encouraged to share knowledge, data, and innovative solutions for wind energy forecasting technologies and sustainable energy practices worldwide. This comprehensive approach aims to contribute to the advancement of wind energy prediction and foster sustainable energy management practices