A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production
Climate change is pushing an increasing number of nations to use green energy resources,
particularly solar power as an applicable substitute to traditional power sources. However …
particularly solar power as an applicable substitute to traditional power sources. However …
[HTML][HTML] Prediction of energy production level in large pv plants through auto-encoder based neural-network (auto-nn) with restricted boltzmann feature extraction
In general, reliable PV generation prediction is required to increase complete control quality
and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV …
and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV …
Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models
Global electricity consumption has raised in the last century due to many reasons such as
the increase in human population and technological development. To keep up with this …
the increase in human population and technological development. To keep up with this …
Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization
M Pan, C Li, R Gao, Y Huang, H You, T Gu… - Journal of Cleaner …, 2020 - Elsevier
Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage
of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term …
of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term …
[HTML][HTML] Advanced methods for photovoltaic output power forecasting: A review
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting
With the fast expansion of renewable energy system installed capacity in recent years, the
availability, stability, and quality of smart grids have become increasingly important. The …
availability, stability, and quality of smart grids have become increasingly important. The …
Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
A main challenge towards ensuring large-scale and seamless integration of photovoltaic
systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high …
systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high …
A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique
K Shivam, JC Tzou, SC Wu - Energy Conversion and Management, 2021 - Elsevier
This paper proposes a multi-objective predictive energy management strategy based on
machine learning technique for residential grid-connected hybrid energy systems. The …
machine learning technique for residential grid-connected hybrid energy systems. The …