An Advanced Transformer Model-Based LSTM-CNN for Power Consumption and PV Power Generation Forecasting in Modern Microgrid
- 9 March 2026
- journal article
- research article
- Published by Springer Nature in Iranian Journal of Science and Technology, Transactions of Electrical Engineering
- p. 1-16
Abstract
No abstract availableKeywords
This publication has 33 references indexed in Scilit:
- Adaptive neuro-fuzzy inference system for accurate power forecasting for on-grid photovoltaic systems: A case study in Sharjah, UAEEnergy Conversion and Management: X, 2025
- Achieving the goals of energy arbitrage, peak-shaving, and PV self-consumption using PV-BTM BESS microgrids coupled with a distribution networkJournal of Energy Storage, 2025
- A systematic review for transformer-based long-term series forecastingArtificial Intelligence Review, 2025
- A new IGDT-based robust model for day-ahead scheduling of smart power system integrated with compressed air energy storage and dynamic rating of transformers and linesJournal of Energy Storage, 2024
- A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecastingElectrical Engineering, 2024
- Residential load forecasting using wavelet and collaborative representation transformsApplied Energy, 2019
- Drivers of domestic electricity users’ price responsiveness: A novel machine learning approachApplied Energy, 2019
- Accurate photovoltaic power forecasting models using deep LSTM-RNNNeural Computing and Applications, 2017
- Short-term load forecasting using a kernel-based support vector regression combination modelApplied Energy, 2014
- Comparison between detailed model simulation and artificial neural network for forecasting building energy consumptionEnergy and Buildings, 2008