首页|Reports Outline Machine Learning Study Findings from Islamic Azad University [Comparative study of long short-term memory (LSTM), bidirectional LSTM, and trad itional machine learning approaches for energy consumption prediction]
Reports Outline Machine Learning Study Findings from Islamic Azad University [Comparative study of long short-term memory (LSTM), bidirectional LSTM, and trad itional machine learning approaches for energy consumption prediction]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from Qazvin, Iran, by Ne wsRx journalists, research stated, “Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring t he reliability of the modern electricity grid.” Our news editors obtained a quote from the research from Islamic Azad University : “This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series d ata often exhibit complex, non-linear patterns. Our approach advocates for utili zing long short-term memory (LSTM) and bidirectional long short-term memory (Bi- LSTM) models for precise time series forecasting. To ensure a fair evaluation, w e compare the performance of our proposed approach with traditional neural netwo rks, time-series forecasting methods, and conventional decline curves. Additiona lly, individual models based on LSTM, Bi-LSTM, and other machine learning method s are implemented for a comprehensive assessment. Experimental results consisten tly demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets.”
Islamic Azad UniversityQazvinIranA siaCyborgsEmerging TechnologiesMachine Learning
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