Review on probabilistic forecasting of photovoltaic power production and electricity consumption
DW Van der Meer, J Widén, J Munkhammar - Renewable and Sustainable …, 2018 - Elsevier
Abstract tAccurate forecasting simultaneously becomes more important and more
challenging due to the increasing penetration of photovoltaic (PV) systems in the built …
challenging due to the increasing penetration of photovoltaic (PV) systems in the built …
A review of machine learning applications in IoT-integrated modern power systems
A review of machine learning applications in IoT-integrated modern power systems -
ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books Search …
ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books Search …
Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast
MS Hossain, H Mahmood - Ieee Access, 2020 - ieeexplore.ieee.org
In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
Credible capacity calculation method of distributed generation based on equal power supply reliability criterion
J Chen, B Sun, Y Li, R Jing, Y Zeng, M Li - Renewable Energy, 2022 - Elsevier
Increasing distributed generation (DG) enables distribution network (DN) carry more load.
Therefore, DG includes both electricity and capacity values. The DG capacity value can be …
Therefore, DG includes both electricity and capacity values. The DG capacity value can be …
Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation
Accurate customer baseline load (CBL) estimation is critical for implementing incentive-
based demand response (DR) programs. The increasing penetration of grid-tied distributed …
based demand response (DR) programs. The increasing penetration of grid-tied distributed …
Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification
M Yu, D Niu, K Wang, R Du, X Yu, L Sun, F Wang - Energy, 2023 - Elsevier
A reliable short-term forecast of photovoltaic power (PVPF) is essential to maintaining stable
power systems and optimizing power grid dispatch. A hybrid prediction framework of PVPF …
power systems and optimizing power grid dispatch. A hybrid prediction framework of PVPF …
[HTML][HTML] Probabilistic solar irradiance forecasting based on XGBoost
X Li, L Ma, P Chen, H Xu, Q Xing, J Yan, S Lu, H Fan… - Energy Reports, 2022 - Elsevier
Solar energy has received increasing attention as renewable clean energy in recent years.
Power grid operators and researchers widely value probabilistic solar irradiance forecasting …
Power grid operators and researchers widely value probabilistic solar irradiance forecasting …
Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets
JF Toubeau, J Bottieau, F Vallée… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In the current competition framework governing the electricity sector, complex dependencies
exist between electrical and market data, which complicates the decision-making procedure …
exist between electrical and market data, which complicates the decision-making procedure …
Convolutional graph autoencoder: A generative deep neural network for probabilistic spatio-temporal solar irradiance forecasting
M Khodayar, S Mohammadi… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Machine learning on graphs is an important and omnipresent task for a vast variety of
applications including anomaly detection and dynamic network analysis. In this paper, a …
applications including anomaly detection and dynamic network analysis. In this paper, a …
A Practical Approach for Predicting Power in a Small‐Scale Off‐Grid Photovoltaic System using Machine Learning Algorithms
Climate change and the energy crisis substantially motivated the use and development of
renewable energy resources. Solar power generation is being identified as the most …
renewable energy resources. Solar power generation is being identified as the most …