Ayene Seblewongale Mezgebu, Yibre Abdulkerim Mohammed
Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar University, P.O.box 26, Bahir Dar, Ethiopia.
Highland College Bahir Dar, Bahir Dar, Ethiopia.
Heliyon. 2024 Oct 18;10(21):e39579. doi: 10.1016/j.heliyon.2024.e39579. eCollection 2024 Nov 15.
Wind is a renewable energy source that is used to generate electricity. Wind power is one of the suitable solutions for global warming since it is free from pollution, doesn't cause greenhouse effects, and it is a natural source of energy. However, Wind power generation highly depends on weather conditions. It is very difficult to easily predict the amount of power generated from wind at a particular instant in time. Adama wind power farm is one of the wind farms in Ethiopia. There is no accurate and reliable forecasting model for the Adama wind farm that enables the forecasting of the power generated from the farm. The main objective of this research is to develop a wind power forecasting model for the Adama wind farm using deep learning techniques. Forecasting of wind power generation capacity involves appropriate modeling techniques that use past wind power generation data. The experiments have been conducted using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). To achieve the highest forecasting accuracy, four years of data (from 2016 to 2019), with 5-min intervals, have been collected with a total of 163,802 rows. For hyperparameter optimization grid search and random search techniques have been utilized. The performances of the proposed deep learning models were investigated error metrics, including Mean Absolute Errors (MAE) and the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R). Bi-LSTM outperforms the other two algorithms, scoring 0.644, 0.388, 0.769 and 0.978 MAE, MAPE, RMSE and R values respectively. Such wind power forecasting helps energy planners and regional power providers to compute power production and energy generated from other sources.
风能是一种用于发电的可再生能源。风力发电是应对全球变暖的合适解决方案之一，因为它无污染、不会造成温室效应，且是一种天然能源。然而，风力发电高度依赖天气条件。很难轻易预测特定时刻风力产生的电量。阿达马风力发电厂是埃塞俄比亚的风力发电厂之一。对于阿达马风力发电厂，没有能够预测该发电厂发电量的准确可靠的预测模型。本研究的主要目标是使用深度学习技术为阿达马风力发电厂开发一个风力发电预测模型。风力发电能力的预测涉及使用过去风力发电数据的适当建模技术。实验使用了长短期记忆网络（LSTM）、双向长短期记忆网络（Bi-LSTM）和门控循环单元（GRU）进行。为了实现最高的预测精度，收集了四年（2016年至2019年）、间隔为5分钟的数据，总共163,802行。对于超参数优化，采用了网格搜索和随机搜索技术。通过误差指标，包括平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）和决定系数（R），对所提出的深度学习模型的性能进行了研究。Bi-LSTM的性能优于其他两种算法，其MAE、MAPE、RMSE和R值分别为0.6
