Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data

(2021) Shalika Walker et al.   ENERGY

Ensemble learning with diversified base models for fault diagnosis in nuclear power plants

(2021) Jiangkuan Li et al.   ANNALS OF NUCLEAR ENERGY

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

(2020) R. Ahmed et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

A review on renewable energy and electricity requirement forecasting models for smart grid and buildings

(2020) Tanveer Ahmad et al.   Sustainable Cities and Society

Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance

(2020) Pratima Kumari et al.   JOURNAL OF CLEANER PRODUCTION

A systematic analysis of meteorological variables for PV output power estimation

(2020) Tarek AlSkaif et al.   RENEWABLE ENERGY

A Survey of Machine Learning Models in Renewable Energy Predictions

(2020) Jung-Pin Lai et al.   Applied Sciences-Basel

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

(2020) Abdelkader Dairi et al.   Applied Sciences-Basel

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

(2019) Konrad Bogner et al.   Sustainability

A hybrid deep learning model for short-term PV power forecasting

(2019) Pengtao Li et al.   APPLIED ENERGY

Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests

(2018) Dan Assouline et al.   APPLIED ENERGY

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

(2018) Xiangyun Qing et al.   ENERGY

A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree

(2018) Jidong Wang et al.   Applied Sciences-Basel

Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression

(2018) Muhammad Waseem Ahmad et al.   ENERGY

Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose

(2018) Zhiyuan Ma et al.   PLoS One

Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

(2017) S. Leva et al.   MATHEMATICS AND COMPUTERS IN SIMULATION

Points of Significance: Ensemble methods: bagging and random forests

(2017) Naomi Altman et al.   NATURE METHODS

Multi-site solar power forecasting using gradient boosted regression trees

(2017) Caroline Persson et al.   SOLAR ENERGY

Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions

(2017) Jose R. Andrade et al.   IEEE Transactions on Sustainable Energy

An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands

(2016) A.G.R. Vaz et al.   RENEWABLE ENERGY

Multi-Model Ensemble for day ahead prediction of photovoltaic power generation

(2016) Marco Pierro et al.   SOLAR ENERGY

A suite of metrics for assessing the performance of solar power forecasting

(2015) Jie Zhang et al.   SOLAR ENERGY

A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

(2014) F. Almonacid et al.   ENERGY CONVERSION AND MANAGEMENT

A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant

(2013) M. Bouzerdoum et al.   SOLAR ENERGY

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

(2012) Jie Shi et al.   IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS

Online 24-h solar power forecasting based on weather type classification using artificial neural network

(2011) Changsong Chen et al.   SOLAR ENERGY

Estimating the uncertainty in long-term photovoltaic yield predictions

(2011) Didier Thevenard et al.   SOLAR ENERGY