Subtopic Deep Dive
Artificial Neural Networks for Solar Radiation Forecasting
Research Guide
What is Artificial Neural Networks for Solar Radiation Forecasting?
Artificial Neural Networks for Solar Radiation Forecasting applies recurrent, convolutional, and hybrid neural architectures to predict global horizontal irradiance and photovoltaic output using meteorological data.
Researchers employ LSTM, GRU, and feedforward networks for short-term forecasting horizons from hours to days. Key studies include LSTM models under varied weather (Yu et al., 2019, 297 citations) and GRU-based PV predictions (Wang et al., 2018, 267 citations). Over 20 papers from 2014-2020 compare ANN variants against physical models, with hybrid approaches gaining traction.
Why It Matters
ANN-based solar forecasting optimizes grid integration of PV systems, minimizing backup fossil fuel use during intermittency (Hong et al., 2020). Accurate predictions enable day-ahead scheduling, as shown in ANN comparisons for 24-hour PV output (Leva et al., 2015; Nespoli et al., 2019). De Giorgi et al. (2014) highlight weather data impacts on forecast reliability for grid management.
Key Research Challenges
Handling Weather Variability
Intermittent cloud cover and atmospheric conditions degrade ANN forecast accuracy. Yu et al. (2019) note LSTM struggles with complicated weather volatility. Hybrid physics-ANN models address this but require extensive tuning (Band et al., 2019).
Hyperparameter Optimization
Optimal architectures and learning rates vary by site and horizon. Leva et al. (2015) validate 24-hour ANN forecasts needing dataset-specific tuning. Nespoli et al. (2019) compare techniques, showing no universal best ANN configuration.
Data Scarcity and Quality
Limited high-resolution irradiance data hinders training. De Giorgi et al. (2014) emphasize weather data quality impacts on Elman networks. Sengupta et al. (2015) outline best practices for solar resource data collection to support ANN inputs.
Essential Papers
Energy Forecasting: A Review and Outlook
Tao Hong, Pierre Pinson, Yi Wang et al. · 2020 · IEEE Open Access Journal of Power and Energy · 529 citations
Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewab...
Comparison of different physical models for PV power output prediction
Alberto Dolara, Sonia Leva, Giampaolo Manzolini · 2015 · Solar Energy · 382 citations
Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power
Sonia Leva, Alberto Dolara, Francesco Grimaccia et al. · 2015 · Mathematics and Computers in Simulation · 302 citations
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources
Shahab S. Band, Timon Rabczuk, Kwok‐wing Chau · 2019 · IEEE Access · 297 citations
Nowadays, learning-based modeling system is adopted to establish an accurate prediction model for renewable energy resources. Computational Intelligence (CI) methods have become significant tools i...
An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions
Yunjun Yu, Junfei Cao, Jianyong Zhu · 2019 · IEEE Access · 297 citations
Complicated weather conditions lead to intermittent, random and volatility in photovoltaic (PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is considered to be a...
Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea
Mohammed H. Alsharif, Mohammad K. Younes, Jeong Kim · 2019 · Symmetry · 275 citations
Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated movin...
Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting
Yusen Wang, Wenlong Liao, Yuqing Chang · 2018 · Energies · 267 citations
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distri...
Reading Guide
Foundational Papers
Start with De Giorgi et al. (2014) for Elman ANN basics and weather impacts; then Saberian et al. (2014) for GRNN/FFBP PV modeling.
Recent Advances
Hong et al. (2020) review for outlook; Yu et al. (2019) LSTM and Wang et al. (2018) GRU for state-of-art short-term methods.
Core Methods
Core techniques: LSTM/GRU for sequences (Yu 2019, Wang 2018), feedforward ANNs (Leva 2015), hybrids with ARIMA (Nespoli 2019).
How PapersFlow Helps You Research Artificial Neural Networks for Solar Radiation Forecasting
Discover & Search
Research Agent uses searchPapers and exaSearch to find ANN solar forecasting papers, starting with 'LSTM solar irradiance' to retrieve Yu et al. (2019). citationGraph reveals connections from Hong et al. (2020) review to Leva et al. (2015) and Wang et al. (2018), while findSimilarPapers expands to GRU hybrids.
Analyze & Verify
Analysis Agent applies readPaperContent to extract LSTM architectures from Yu et al. (2019), then verifyResponse with CoVe checks forecast metrics against De Giorgi et al. (2014). runPythonAnalysis recreates MAE/RMSE via NumPy on PV datasets, with GRADE scoring evidence strength for hybrid model claims.
Synthesize & Write
Synthesis Agent detects gaps like site-specific tuning from Nespoli et al. (2019), flagging contradictions in weather handling. Writing Agent uses latexEditText for equations, latexSyncCitations for 10+ papers, and latexCompile for forecast comparison tables; exportMermaid visualizes ANN architecture flows.
Use Cases
"Reproduce RMSE from Yu et al. 2019 LSTM solar forecasting in Python"
Research Agent → searchPapers('Yu LSTM solar') → Analysis Agent → readPaperContent → runPythonAnalysis (NumPy/pandas RMSE calc on irradiance data) → matplotlib plot of reproduced vs. reported metrics.
"Write LaTeX section comparing ANN vs. ARIMA for PV forecasting"
Synthesis Agent → gap detection (ANN superiority per Band 2019) → Writing Agent → latexEditText (draft text) → latexSyncCitations (Leva 2015, Alsharif 2019) → latexCompile (PDF with tables).
"Find GitHub code for GRU PV forecasting models"
Research Agent → searchPapers('GRU solar Wang 2018') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect (extracts training scripts, datasets from Wang et al. 2018 repos).
Automated Workflows
Deep Research workflow scans 50+ ANN papers via searchPapers → citationGraph, producing structured report on LSTM/GRU evolution (Yu 2019 to Hong 2020). DeepScan applies 7-step CoVe to verify hybrid claims in Nespoli et al. (2019), with GRADE checkpoints. Theorizer generates hypotheses on physics-informed NNs from De Giorgi (2014) and Sengupta (2015) data practices.
Frequently Asked Questions
What defines Artificial Neural Networks for Solar Radiation Forecasting?
It covers RNNs like LSTM and GRU applied to predict GHI and PV output from weather inputs, as in Yu et al. (2019) and Wang et al. (2018).
What are common ANN methods in this subtopic?
LSTM for time-series (Yu et al., 2019), Elman networks with weather data (De Giorgi et al., 2014), and hybrid ANN-ARIMA (Nespoli et al., 2019).
What are key papers?
Foundational: De Giorgi et al. (2014, 260 citations); Recent: Hong et al. (2020, 529 citations), Yu et al. (2019, 297 citations).
What are open problems?
Improving forecasts under cloudy conditions and scaling hybrids without site-specific tuning, per Band et al. (2019) and Leva et al. (2015).
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Part of the Solar Radiation and Photovoltaics Research Guide