An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Liu, Yuwei; Feng, Huan; Li, Heng-Yi; L, Li

doi:10.3390/sym13020212

Cited by 17 publications

(8 citation statements)

References 49 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WOA consists of three phases: encircling the prey, bubble-net attacking, and searching for the prey. WOA has been used to solve a wide range of optimization problems in different applications including feature selection [28], software defect prediction [29], clustering [30,31], classification [32,33], disease diagnosis [34], image segmentation [35,36], scheduling [37], forecasting [38,39], parameter estimation [40], global optimization [41], and photovoltaic energy generation systems [42,43]. Even though WOA is employed to tackle a wide variety of optimization problems, it still has flaws such as premature convergence, the imbalance between exploration and exploitation, and local optima stagnation [44,45].…”

Section: Introductionmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

View full text Add to dashboard Cite

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The time series methods included different methods, such as Kalman filter, 19 Support Vector Regression (SVR), 20 Grey Forecasting Method, 21 Auto-Regressive Integrated Moving Average (ARIMA), 22 and Hidden-Markov Models (HMM). 23 The learning methods comprised the Artificial Neural Network (ANN), 24,25 Support Vector Machine (SVM), 26 Wavelet Analysis (WA), 27 and Fuzzy Logic (FL). 28 The ANN is deemed one of the most popular statistical methods adopted to predict the PV generation with a prediction horizon of 24-h ahead.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

show abstract

“…The study demonstrates that the symmetry-based long forecast model for solar panels achieves complete accuracy under a range of meteorological circumstances. The structured technique to predicting renewable output will promote the utilization of clean power and economic growth by reducing the burden of anticipating conversion efficiency [10].…”

Section: Related Workmentioning

confidence: 99%

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Ramkumar

Sahoo

Amirthalakshmi³

et al. 2021

International Journal of Photoenergy

View full text Add to dashboard Cite

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

show abstract

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Cited by 17 publications

References 49 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Contact Info

Product

Resources

About