Data-driven short-term forecasting of solar irradiance profile

Loh, Poh Soon; Chua, Jialing Vivien; Tan, Aik Chong; Khaw, Cheng Im

doi:10.1016/j.egypro.2017.12.729

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Solar irradiation forecasting has been addressed through different AI techniques: artificial neural networks (ANNs), which simulate human brain abilities [2]; wavelet neural networks (WNN), which consist of a combination of wavelet theory and neural networks [5]; support vector machines (SVM) [13] or fuzzy logic [14], which are associated with the generalization of classical logic. Moreover, there are techniques that are based on the combination of these methods [15].…”

Section: Artificial Intelligencementioning

confidence: 99%

Optimization of a solar irradiation forecasting tool based on artificial intelligence

Rodríguez¹,

Ceit²,

Spain³

et al. 2019

REPQJ

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In current electric markets, where many stakeholders can take part, power network operators need accurate predictions of the energy generated by intermittent renewable sources in order to control the whole system. Therefore, the capacity to accurately forecast solar irradiance is key when it comes to the large-scale integration of solar energy generators in the traditional network. One of the challenges, however, consists of providing accurate very short-term predictions (minutes ahead) due to the variability of solar irradiance caused by different meteorological phenomena. This study addresses this need for very short-term forecasts through the development of an irradiance prediction scheme for 10 minutes ahead. The irradiance prediction algorithm is based on a parallel combination of two different layer recurrent networks and has been trained with a two-year historical database of solar irradiance. The accuracy of the proposed tool has been validated through forecasting a whole year using data that is not in the database used in the training step. This tool was then used to forecast the irradiance in two Spanish locations with different weather conditions to analyse whether the accuracy changes. The accuracy between predicted and actual values demonstrates that this tool outperforms similar forecasters.

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Section: Artificial Intelligencementioning

confidence: 99%

Optimization of a solar irradiation forecasting tool based on artificial intelligence

Rodríguez¹,

Ceit²,

Spain³

et al. 2019

REPQJ

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“…In this category, articles are presented like BPNN (Li et al [23]), WTBPNN (Changhao et al [24]), GNBPNN (Irani et al [25]), NNPSO (Zhaoyu et al [26]), WT-ANFIS (Karthika et al [27]), ADE-BPNN (Wang et al [28]), Wavelet-PSO-ANFIS,) Catalao et al [29]), and WT-PSO-BPNN (Mandel et al [30]) whose goal is to arrive at the highest accuracy in forecasting. Some other research are found in [10,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Figure 1 shows the development of the new technique.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Salami

Sobhani

Ghazizadeh

2018

Data

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The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

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