Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
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[1] Yan Xu,et al. Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties , 2019, Applied Energy.
[2] Xuebin Wang,et al. Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems , 2018, Applied Energy.
[3] Cunbin Li,et al. A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting , 2016, Applied Intelligence.
[4] Wei Li,et al. Decomposition and forecasting analysis of China's energy efficiency: An application of three-dimensional decomposition and small-sample hybrid models , 2015 .
[5] Frederico G. Guimarães,et al. Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series , 2019, Energy.
[6] Frédéric Kuznik,et al. Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling , 2019, Applied Energy.
[7] Yan Li,et al. Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm , 2018 .
[8] Abdellatif Miraoui,et al. Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation , 2017 .
[9] Hal Gurgenci,et al. Search for optimum renewable mix for Australian off-grid power generation , 2019, Energy.
[10] Shie-Jue Lee,et al. A weighted LS-SVM based learning system for time series forecasting , 2015, Inf. Sci..
[11] Mayur Barman,et al. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India , 2018 .
[12] Xiaodong Liang. Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources , 2017 .
[13] Dan Wang,et al. Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations , 2019, Applied Energy.
[14] Qiang Yang,et al. Scenario-based investment planning of isolated multi-energy microgrids considering electricity, heating and cooling demand , 2019, Applied Energy.
[15] Yi Liang,et al. Short term load forecasting based on feature extraction and improved general regression neural network model , 2019, Energy.
[16] Durga Toshniwal,et al. Deep learning framework to forecast electricity demand , 2019, Applied Energy.
[17] Yi Yang,et al. A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting , 2015 .
[18] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[19] Jing Wang,et al. Research on DC bias analysis for transformer based on vibration Hilbert Huang transform and ground-state energy ratio method , 2019, International Journal of Electrical Power & Energy Systems.
[20] Ming-Lang Tseng,et al. Renewable energy prediction: A novel short-term prediction model of photovoltaic output power , 2019, Journal of Cleaner Production.
[21] Song Ding,et al. Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model , 2019, Energy.
[22] Huanran Dong,et al. Optimal design of integrated energy system considering economics, autonomy and carbon emissions , 2019, Journal of Cleaner Production.
[23] Emmanuel Kakaras,et al. Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system , 2019, Applied Energy.
[24] Yunfei Mu,et al. Energy-Internet-oriented microgrid energy management system architecture and its application in China , 2018, Applied Energy.