Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine: A case study of Tibet area in China

Jiang, He; Dong, Yao

doi:10.1016/j.enconman.2017.03.054

Cited by 38 publications

(6 citation statements)

References 31 publications

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“…There is a range of ML algorithms that have been used in solar irradiance prediction, such as regression, Markov chain [17], autoregressive integrated moving average (ARIMA) [18], and neural networks [19]. One of the most commonly used ML algorithms is the support vector machine (SVM) [12,[20][21][22]. The SVM model is a conventional algorithm that has been used for more than a decade to predict solar irradiance [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

et al. 2021

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Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.

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Section: Literature Reviewmentioning

confidence: 99%

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

et al. 2021

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“…Statistical methods are mainly data‐driven. The forecast models are established based on the historical data of wind power, including Autoregressive Moving Average model (ARMA) [3], Support Vector Machine (SVM) [4], and Artificial Neural Network (ANN) [5].…”

Section: Introductionmentioning

confidence: 99%

Wind power forecast based on broad learning system and simplified long short term memory network

Han

Wang

et al. 2022

IET Renewable Power Gen

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Due to its strong randomness and volatility, the modes of wind power are complex. After decomposing wind power time series into three subseries, the complex modes are deconstructed and each subseries maintain unique characteristics. Aiming at the characteristics of each subseries, a wind power forecast method based on Broad Learning System (BLS) and Simplified Long Short Term Memory (SLSTM) is proposed. Firstly, the decomposed subseries is analysed. The first layer subseries reflects the main amplitude changes caused by ramp events and the second represents the local fluctuations accompanying ramp events. Due to the randomness of ramp events, new modes appear frequently in the two subseries. The third layer subseries consists of continuous irregular jitter. Then, the BLS is introduced to forecast the first and second layer subseries. By adjusting its structure dynamically, various modes of ramp events can be learned from amplitude change and accompanying fluctuation. Additionally, according to the characteristics of the third layer subseries, continuous jitter needn't be transmitted in long‐term memory. Setting the forget gate to 0, SLSTM is proposed to forecast the third layer subseries. Finally, the wind power data from the Belgian ELIA website is used to verify the superiority of the proposed method.

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“…However, due to the chaotic nature of the weather system, the production of photovoltaic energy is highly random, volatile and intermittent, which may lead to grid power and voltage imbalances, and also greatly increase the difficulty of large-scale photovoltaic energy applications [6] [7]. In order to improve the power system's ability to consume photovoltaic energy, many solutions have been proposed, including energy storage optimization [8], demand response strategy [9] [10], power flow optimization [11], stand-alone microgrid [12], and PV power forecasting [13].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

Lin¹,

Li²

2021

JCC

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Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine: A case study of Tibet area in China

Cited by 38 publications

References 31 publications

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Wind power forecast based on broad learning system and simplified long short term memory network

A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

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