Forecasting Solar Radiation Strength Using Machine Learning Ensemble

Al-Hajj, Rami; Assi, Ali; Fouad, Mohamad M.

doi:10.1109/icrera.2018.8567020

Cited by 21 publications

(11 citation statements)

References 14 publications

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“…Steps (8)(9)(10)(11)(12)(13)(14) update the position of different agents by using Eq. 19 where rand SC > 0.5 while steps (17)(18)(19) update the position of different agents by using Eq. 21 for rand SC ≤ 0.5.…”

Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

“…21 for rand SC ≤ 0.5. Steps (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) are repeated in the Algorithm (2) until a predefined criterion is met. The best solution P in Step 5 will be updated by exploring and exploiting the around space.…”

Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

“…Geographical and meteorological variables of a specific location are the key parameters considered for solar radiation's forecasting [16]- [18]. Al-Hajj et al proposed a predictive model based on Dynamic Recurrent Neural Networks (DRNN) with short-term delay units to forecast the daily intensity of solar radiations [19]. The model provided better results than Root Mean Square Error (RMSE) and Mean Bias Error (MBE).…”

Section: Introductionmentioning

confidence: 99%

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Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

et al. 2021

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As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton's laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent's position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon's rank-sum tests.

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Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

Section: B Advanced Sine Cosine Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

et al. 2021

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“…India has even established a special ministry for RES; Ministry of New and Renewable Energy (MNRE), with a goal of generating 175 GW of energy from RES by the end of 2022; with 100 GW from solar alone [2,3]. Furthermore, according to several studies, the power grid will be completely functioning on the renewable energy source (RES) by the end of 2050 [4]. But, due to the variability in weather condition; the intensity of solar GHI is unstable which directly affect the output of photovoltaic power plant [5].…”

Section: Introductionmentioning

confidence: 99%

Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

Gupta

Saroha

2023

SPEE

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Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.

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“…For example, a stacking ensemble learning model was adopted to predict the short-term wireless network load and car-hailing demand [46,47]. In [48], a stacking-based ensemble model was deployed to forecast solar radiation strength. Liang et al [49] and Al-Sarem et al [50] adopted a stacking learning framework for genomic prediction and phishing website detection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis

Yang

Liu

Zeng

et al. 2022

Scientific Programming

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The purpose of spare parts management is to maximize the system’s availability and minimize the economic costs. The problem of cost availability trade-off leads to the problem of spare parts demand prediction. Accurate and reasonable spare parts demand forecasting can realize the balance between cost and availability. So, this paper focuses on spare parts management during the equipment normal operation phase and tries to forecast the demand of spare parts in a specific inspection and replacement cycle. Firstly, the equipment operation and support scenarios are analyzed to obtain the supportability data related to spare parts requirements. Then, drawing on the idea of ensemble learning, a new feature selection method has been designed, which can overcome the limitations of a single feature selection method. In addition, an improved stacking model is proposed to predict the demand for spare parts. In the traditional stacking model, there are two levels of learning, base-learning, and meta-learning, in which the outputs of base learners are taken as the input of the meta learner. However, the proposed model brings the initial feature together with the output of the base learner layer as the input of the meta learner layer. And experiments have shown that the performance of the improved stacking model is better than the base learners and the traditional stacking model on the same data set.

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Forecasting Solar Radiation Strength Using Machine Learning Ensemble

Cited by 21 publications

References 14 publications

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

An Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis

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