Sep 30, 2024 ˇ In their study, Yan et al [20] employed the Bi-LSTM model as a power load prediction model to enhance the accuracy of forecasting results. Some ...
Aug 29, 2024 ˇ It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and ...
In the definition, machine learning learns from experience E, namely the data, making it a data-driven, black-box, or empirical method. In building load.
Abstract—This paper presents a generic strategy for short-term load forecasting (STLF) based on the support vector regression machines (SVR).
Results: Through the study, it is understood that hybrid methods show promising features. Deep learning algorithms were also studied for long-term forecasting.
Study on 'Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network (LSTMN) and Convolutional Neural Network (CNN)'. A combined ...
Missing: Production | Show results with:Production
Jun 8, 2023 ˇ The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural ...
Abstract. Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been ...
Missing: Generic | Show results with:Generic
Abstract—This paper presents a generic strategy for short-term load forecasting (STLF) based on the support vector regression machines (SVR).
Sep 23, 2023 ˇ In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, ...