solar-intensity-forecasting


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Solar power forecasting is at its core a weather forecasting problem. It starts with modeling of the Sun’s position with respect to a given location at the Earth’s surface, before then modeling the transmittance of solar radiation through the Earth’s atmosphere.

Intra-hour irradiance forecasting techniques for solar power
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by Y Chu The fundamental considerations include the following: solar irradiance monitoring, clear-sky models and clear sky index (Section S1), common inputs for solar

Forecasting Solar Irradiance from Time Series with
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by K Ariga Solar irradiance forecast is the pivotal factor for power generation scheduling at photovoltaic (PV) power plants. To this end, we propose a framework to

Short-Term Deterministic Solar Irradiance Forecasting MDPI
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by A Castillejo-Cuberos A novel solar radiation forecasting method was developed based on pattern identifica- tion and classification, probability and heuristic

Short-Term Solar Irradiance Forecasting via OSTI.GOV
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by SD Miller 73 The short-term (0 3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques.

solar irradiance forecasting for the management of solar
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by D Heinemann 19 First attempts in irradiance forecasting have been presented more than twenty years ago (Jensenius and Cotton, 1981), when daily solar radiation forecasts for

A model of very short-term solar irradiance forecasting based
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by Y Ai 10 Then use the optical flow and adaptive threshold scheme for cloudy days to forecast the future movement of clouds, predict the sun obscured situation in the

Review of solar irradiance forecasting methods and a
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by HM Diagne 631 forecasting approaches dedicated to the management of small-scale insu- lar grids. Keywords: Solar irradiance , forecast models,

Solar Irradiance Forecasting Using Triple Exponential arXiv
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by S Dev 20 Index Terms Photovoltaic, Solar energy analytics, Clear sky model, Intra-hour forecasting , Exponential smoothing. I. INTRODUCTION. Due to the

Implementation of Solar Irradiance Forecasting Using Markov
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by A Shakya 1 In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally

Solar Irradiance Forecasting Based on Deep Learning MDPI
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by B Brahma 20 focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL)

Forecasting of global and direct solar irradiance using
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by R Marquez 319 We develop and validate a medium-term solar irradiance forecasting model by adopting predicted meteorological variables from the.

SHORT-RANGE SOLAR RADIATION FORECASTS IN
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by GD Modica 6 In this paper we describe a prospective solar radiation forecast system that integrates cloud analysis, prediction , and radiative transfer.

Evaluating solar radiation forecast uncertainty
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by M Tuononen 10 Many applications require accurate forecasts of surface ra- diation on weather timescales, for example solar energy and. UV radiation forecasts .

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of
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by W Traunmüller 20 Solar irradiance forecast can be accomplished with completely different methods, In this paper the use of solar irradiance and weather forecasts to. by Z Zhang 1 INDEX TERMS Solar radiation intensity, K-means time series clustering, probabilistic forecasting , Gaussian process regression.

Short-term forecasting of solar irradiance without local telemetry
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by J Lagoa 36 Keywords: Solar Irradiance Forecast , Generalized Model, Deep Learning, Satellite Data. Acronyms The forecasting of solar irradiance can be typically di-.

Use of physics to improve solar forecast Atmospheric
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by W Liu 5 c National Renewable Energy Laboratory, Golden, CO, USA. A R T I C L E I N F O. Keywords: Physics-informed persistence models. Solar irradiance forecasting .

Predicting solar radiation based on available weather indicators
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by FJ Sauer 2 Solar radiation prediction models are complex and require software that is not available for the household investor. The processing power within a normal

Advancing the WRF-Solar Model to Improve Solar Irradiance
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One Goal. Improve the state of art WRF- Solar model for forecasting solar irradiance in cloudy environment. ❖ Four Objectives. Improve cloud microphysics.

SOLAR RADIATION PREDICTION USING LSTM AND CNN A
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by H Patel Lasenby, Convolutional Neural Networks applied to sky images for short- term solar irradiance forecasting , in Proceedings of the European. PV Solar

Regime-Dependent Short-Range Solar Irradiance Forecasting
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by TC MCCANDLESS 29 Regime-Dependent Short-Range Solar Irradiance Forecasting . T. C. MCCANDLESS. National Center for Atmospheric Research,a Boulder, Colorado, and Department of

Solar Irradiance Prediction Using Xg-boost With the Numerical
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by PS Kamarouthu regression and gradient boost algorithms like, xg-boost and cat-boost to weather forecast dataset from GFS (Global Forecast System), to predict solar energy a predictive model that effectively observes solar energy conversion with high performance make utilizing solar energy more efficient, forecasting .

TOWARDS A GENERIC ONTOLOGY FOR SOLAR
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by A Kantamneni The growth of solar energy resources in recent years has led to increased calls for accurate forecasts of solar irradiance for the reliable and sustainable

Estimating solar irradiance using sky imagers
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The accurate estimation and prediction of solar energy generation is a challenging task, as clouds greatly impact the total irradiance received

A Survey Paper on Solar Irradiance Forecasting Methods
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accurate prediction of solar irradiance this type of collaboration By using historical data for solar irradiance forecasting it may.

Solar Irradiance Forecasting Using Intelligent Technology
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Abstract Because solar power is susceptible to clouds and substances in the air, the solar photovoltaic cannot produce stable power output. Solar irradiance

An Open Source Evaluation Framework for Solar Forecasting
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by CW Hansen 7 program and the broader solar forecast community. The framework enables evaluations of solar irradiance , solar power, and net-load forecasts that are

1 Machine Learning methods for solar radiation forecasting
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Keywords: Solar radiation forecasting , machine learning, artificial neural networks, support vector. 29 machines, regression.

Evaluation of ECMWF model to predict daily and monthly solar
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by Y Sianturi Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production.

COMPARISON BETWEEN SEVERAL PARAMETERIZATION
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by I López-Coto 12 The numerical weather prediction (NWP) models have widely demonstrated their usefulness in solar energy forecasting . Nevertheless, the high resolution and

Predicting Solar Power Production:
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by M MAkhyoun 3 Irradiance Forecasting Models, Applications and Future Prospects 3.3.1 Point versus area solar energy plant predictions and PV power output variability

A deep attention-driven model to forecast solar irradiance
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by A Dairi Results confirm the superior performance of the proposed model for solar irradiance forecasting over the other models (i.e., RNN, GRU, LSTM, and. BiLSTM). Index

UNIVERSITY OF CALIFORNIA, SAN DIEGO Solar Forecasting
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by RH Inman Jr 4 Many solar irradiance forecasting models require the knowledge of clear sky conditions. Clear sky models are used for persistence forecasts, discussed in §2. .

30min-Ahead Gridded Solar Irradiance Forecasting using
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by T Taomae solar irradiance forecasting is dependent on meteorological conditions and, in particular, cloud cover, which are captured in satellite imagery.

A Proposed Model to Forecast Hourly Global Solar Irradiation
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by B Benamrou 3 the energy generated by a photovoltaic system. Nowadays, there is a need to develop effec- tive solar radiation forecasting methods for dif-.

A Deep Physical Model for Solar Irradiance Forecasting With
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by V Le Guen 9 We present a new deep learning approach for short-term solar irradiance forecasting based on fisheye images. Our architecture, based on recent works on

Improvement of Coding for Solar Radiation Forecasting in Dili
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by JMS de Araujo 1 improving coding for solar radiation forecasting for location in Dili Timor. Leste. Weather Research and Forecasting (WRF) model version

Hourly Global Solar Irradiation Forecasting for New Zealand A
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by A Ahmad 79 With the growing use of solar thermal energy systems and small scale photovoltaic power generation by domestic users, there is increasing need to develop

A state-of-art method for solar irradiance forecast via using
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by L Chen The conclusion shows that common camera fisheye lens coupled with BP neural network successfully predict the solar irradiance . Keywords: fisheye

An Ontology for Solar Irradiation Forecast Models SciTePress
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by A Kantamneni 2 Abstract: The growth of solar energy resources in recent years has led to increased calls for accurate forecasts of solar irradiance for the reliable and

Predicting solar radiation using a parametric cloud model
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by K Skeie 1 controller for energy management in buildings on actual forecast data. However, a recent review by Du et. al. (2019) shows that solar radiation forecasts

Lasso Based Schemes for Solar Energy Forecasting Auburn
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by T Ningkai Auburn, Alabama. 2021. Keywords: Smart Grid, LASSO, Machine learning, Solar Intensity Forecast , Adversarial. Attack, Renewable Energy.

Sebuah Kajian Pustaka: Bulletin of Electrical Engineering
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by H Suyono 7 Comparison of Solar Radiation Intensity Forecasting Using. ANFIS and Multiple Linear Regression Methods. Hadi Suyono Rini Nur Hasanah R. A. Setyawan

Hourly Solar Radiation Forecasting Through Neural Network
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by CR HAMILTON 4 HOURLY SOLAR RADIATION FORECASTING THROUGH NEURAL NETWORKS AND. MODEL TREES by. CAMERON REID HAMILTON. (Under the Direction of Walter D. Potter). Abstract

Short-term output power forecasting of photovoltaic systems
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by LL Li 30 First, affecting factors of photovoltaic power generation are studied, including solar radiation intensity, air temperature, rela- tive humidity, and wind speed

Analysis of ARMA Solar Forecasting Models Using Ground
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by F Marchesoni-Acland 3 Index Terms Forecasting , solar irradiance , adaptive filters, satellite images. I. INTRODUCTION. Solar energy is being increasingly used as a

Forecasting Localized Weather-Based Photovoltaic Energy
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by K Chang generation forecasting model to predict PV power system performance mance data, weather data and solar insolation data, the amount of

Evaluating solar radiation forecast uncertainty University of
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by M Tuononen 10 Many applications require accurate forecasts of surface ra- diation on weather timescales, for example solar energy and. UV radiation forecasts . Here we

One-Hour Prediction of the Global Solar Irradiance from All
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by C Crisosto 35 Keywords: solar energy; all-sky image; solar irradiance prediction ; artificial neural networks. 1. Introduction. The production of solar

Solar Irradiance Forecasting and Implications for Domestic
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by C Nutter INDEX WORDS: Artificial Neural Network, NAR, NARX, Domestic Electric Water Heat, Solar. Irradiance Forecasting , Solar Energy , Thesis,

Cloud Cover and PV Intermittence: Monitoring, Forecasting
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by A Haegele 2.3 Solar Irradiance Forecasting . 8. 2.4 Persistence Forecasts Cloud Motion Vector Forecasts. 9. 2.5 Numerical Weather Prediction (NWP)

Photovoltaic Power Production Estimation Based on
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by FEA Espejo 1 design solar plants as well as to forecast their performance and energy production. Index Terms forecasting performance, numerical weather.

Prediction of Solar Radiation Based on Spatial and Temporal
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by M Alqudah 1 an accurate real-time forecast of the solar generation so that both higher system operation algorithms were used for solar irradiance forecasting .

Day-ahead forecasting of solar power output from photovoltaic
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by DP Larson 190 High renewable energy penetration grids are challenging to balance due to inherently variable generation weather-dependent energy resources. Solar and wind

Solar Radiation Forecasting Using Artificial Neural Network for
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by X YAN 27 system with some probability or possibility methods. Keywords photovoltaic power; power reserve; solar radiation forecasting ; Artificial Neural Network.

The intensity of solar radiation forecasting based on artificial
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by B Boris Artificial neural networks, as a tool for modelling and forecasting , are widely accepted as an alternative way of.

A Concept-Drift Based Predictive-Analytics Framework
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by J Wojtkiewicz 9 Predicting solar irradiance is very useful for measuring future solar energy production and forecasting techniques have been applied to solar irradiance .

Zhang_2020_SE_SolarNet.pdf The University of Texas at
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Solar forecasting . Sky image processing. A B S T R A C T. The exponential growth of solar energy poses challenges to power systems, mostly due to its

Short-term solar radiation forecasting using a new seasonal
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by H Ali-Ou-Salah Forecasting ; solar radiation ; machine learning; artificial neural network; clustering; fuzzy c-means. 1. Introduction.

Solar power generation short-term forecasting models
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by E Kochneva 2 The electricity amount forecast produced by the solar station is the prediction of the solar radiation amount that will received by solar panels. It depends on

Daily solar radiation forecasting using historical data and
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By having a thought of forecasting solar radiation would be easier comparing to other renewable resources, because it is not related to wind speed orSolar Radiation Forecasting Using Ad-Hoc Time Series. Preprocessing and Neural Networks. ∗. Christophe Paoli Cyril Voyant Marc Muselli

Advanced machine learning techniques for short-term solar
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by P Kumari short-term solar irradiance forecasting . Pratima Kumari∗ pratimatomar93@gmail.com. India Institute of Technology. Roorkee, Uttarakhand, India.

SOLAR IRRADIANCE FORECASTING USING NEURAL
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Keywords: solar irradiance , neural networks, forecasting , prediction , machine learning. The originality of this thesis has been checked using

Solar Radiation Forecasting in a desert environment NOAA
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Abstract. The most widely used tool for solar radiation forecasting from 6 hours up to several days ahead is a Numerical Weather Prediction (NWP) model.

Hourly Day Ahead Solar Irradiance Forecasting Model in
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by O CEYLAN 1 forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, Detailed information on solar irradiance forecasting .

Sensor Network based Solar Forecasting using a Local Vector
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by J Xu 10 Solar Forecast ; Sensor Network; local vector autoregressive (LVAR). 1. INTRODUCTION. Solar energy is emerging as the most promising energy resource.

Solar Power Forecasting with Machine Learning Techniques
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by E Isaksson 7 Keywords: Solar PV Power Prediction , Machine Learning, Time Series, Artificial. Intelligence, Renewable Energy Sources, Forecasting , Solar Forecasting

Matrix Based Univariate And Multivariate Linear Similar Day
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by S Sreeku 6 investigate the similarity of solar radiation of similar days in accurate solar power prediction techniques in Energy Man-.

Forecasting of Solar Electricity Generation and Performance
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by S Chaudhary and Simple Exponential for forecasting solar radiation data. ARIMA have been chosen because it is among the best models which are used for

Artificial Intelligence Methods for Solar Forecasting for
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by S Jogunuri 1 Keywords: PV system, Solar irradiance , optimum sizing, forecasting techniques, artificial intelligence, hybrid techniques. Introduction. PV generation mostly

A Deep Physical Model for Solar Irradiance Cedric/CNAM
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by V Le Guen 9 We present a new deep learning approach for short-term solar irradiance forecasting based on fisheye images. Our architecture, based on recent works on

Predicting Solar Power Generation from Weather Data Project
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1 Specifically, we will use data from the National Solar Radiation Database. (NSRDB), which conveniently includes both weather and solar intensity

Photovoltaic and Solar Forecasting: Meteonorm
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by PV Forecast 1 INTERNATIONAL ENERGY AGENCY. PHOTOVOLTAIC POWER SYSTEMS PROGRAMME. Photovoltaic and Solar Forecasting : State of the Art. IEA PVPS Task 1 Subtask 3.1.

Time series forecasting on multivariate solar radiation data
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by MC SORKUN 13 Depending on the horizon, two main classes of methods can be used to forecast the solar radiation : statistical time series forecasting methods

Short-range solar radiation forecasts over Sweden
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by T Landelius 9 in terms of forecasts of both the amount of solar power and its els when it comes to forecasting solar radiation (Perez et al.,.

An application of MDLPF models for solar radiation forecasting
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by E Akarslan 3 Keywords: Solar radiation , forecasting , MDLPF, linear prediction used 2-D linear filters to forecast the hourly solar radiation data.

Overview of Solar-Forecasting Methods and a Metric for
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Forecast Model. 191. 8.5. Conclusions. 191. References. 192. Solar Energy Forecasting and Resource Assessment. ISBN: 9780123971777.

5-AGWA-8
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by RG Allen 1998 25202 Solar radiation . Air temperature. Air humidity. Wind speed missing radiation data. Missing wind speed data Forecasting or allocating irrigations.

CH- 5
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intensity . communication. tides Solar Eclipse: When the moon comes between the b) It is used for weather forecasting broadcasting TV programs.

Financial Results for the Quarter and Year ended 31st ITC
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lower Covid caseload intensity ; remained significantly above pre-pandemic levels nodes encompassing synchronized planning forecasting ,

Weather Factors Precipitation Answers
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Science of Snowflakes How to Predict the Weather by Reading the Clouds Factors of weather are: sunlight, as are solar radiation , the amount of.

Adaptive Learning Hybrid Model for Solar Intensity Forecasting

Yu Wang, Member, IEEE, Yinxing Shen, Shiwen Mao, Senior Member, IEEE,
Guanqun Cao, and R. M. Nelms, Fellow, IEEE

Abstract—Energy management is indispensable in the smart grid, which integrates more renewable energy resources, such as solar and wind. Because of the intermittent power generation from these resources, precise power forecasting has become very crucial to achieve efficient energy management. In this paper, we propose a novel adaptive learning hybrid model (ALHM) for precise solar intensity forecasting based on meteorological data. We first present a time-varying multiple linear model (TMLM) to capture the linear and dynamic property of the data. We then construct simultaneous confidence bands (SCB) for variable selection. Next we apply the genetic algorithm back propagation neural network (GABP) to learn the nonlinear relationships in the data. We further propose ALHM by integrating TMLM, GABP and the adaptive learning online hybrid algorithm (ALOHA). The proposed ALHM captures the linear, temporal and nonlinear relationships in the data, and keeps improving the predicting performance adaptively online as more data collected. Simulation results show that ALHM outperforms several benchmarks in both short-term and long-term solar intensity forecasting. Index Terms—Solar intensity forecasting, online adaptive learning, local linear estimation, artificial neural network, genetic algorithm back propagation neural network.
I. INTRODUCTION
In recent years, Smart Grid (SG) has become an irreversible tendency in many countries all over the world. The advanced techniques from many fields, including industrial informatics, power electronics and automatic control make SG a sustainable power grid, which integrates more renewable energy sources, such as solar and wind [1], [2]. Because of the intermittency of renewable power generation, energy management is thus very important to improve the reliability, efficiency and utility of a SG [3]–[5]. It is mentioned in [5] that energy management efficiency can be greatly improved if the renewable energy generation can be predicted more accurately [6]–[8]. Thus, predicting renewable energy generation in the SG has attracted great interests [9], [10], mainly focusing on predicting solar power for their wide range of utilization. Solar power generation from solar panels are proportional to solar intensity, power generated per unit area. Therefore, predicted solar power can be acquired by predicting solar intensity, which is related to meteorological variables. Many recent works focus on the meteorological-data-based solar intensity forecasting problem by presenting different methods [2], [11].The work of [2] provides acceptable predicting results using SVM regression, and the author of [11] proposes the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism But it lacks a deep analysis of the solar power generation and weather data. Learning techniques are also used to predict solar intensity, capturing the relationships between solar intensity and the meteorological variables. Artificial neural network (ANN) [12]– [14] is also a commonly used learning algorithm for complex

solar-intensity-forecasting
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