Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Abdullah, Nor Azliana; Rahim, Nasrudin Abd; Gan, Chin Kim; Adzman, Noriah Nor

doi:10.3390/app9163214

Cited by 20 publications

(10 citation statements)

References 41 publications

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“…Similarly, different methods (models) can be used for very short-term or short-term solar (photovoltaic) power prediction. The examples are: using smart persistence and random forests for forecasts of photovoltaic energy production [31]; an ensemble model for short-term photovoltaic power forecasts [32]; hybrid method based on the variational mode decomposition technique, the deep belief network and the auto-regressive moving average model (for short-term solar power forecasts) [33]; a model which combines the wavelet transform, adaptive neuro-fuzzy inference system, and hybrid firefly and particle swarm optimization algorithm (for solar power forecasts) [34]; a physical hybrid artificial neural network for the 24 h ahead photovoltaic power forecast in microgrids [35]; a hybrid solar and wind energy forecasting system on short time scales [36].…”

Section: Related Workmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

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This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

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Section: Related Workmentioning

confidence: 99%

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

et al. 2021

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“…Owing to the responsive impact of such parameter, tuning the PI controller faces great difficulties. Therefore, many metaheuristic algorithms have been improved in order to overcome those difficulties, such as particle swarm optimization (PSO) [8], sunflower optimization algorithm (SFO) [9][10], hybrid GWO-PSO optimization technique [11], genetic algorithm (GA) [12], hybrid firefly and particle swarm optimization technique [13], Harris hawks optimization Method [14], marine predators algorithm [15], hierarchical model predictive control [16], Tabu search [17], quasi-oppositional selfish herd optimization (QSHO) [18], Cuttlefish optimization algorithm (CFA) [19], and teaching-learning based optimization [20]. Each of those techniques has its benefits and drawbacks [21].…”

Section: A Literature Reviewmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

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Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers. INDEX TERMS microgrid; optimization; power systems; renewable energy. Ahmed Al-Durra (S'07-M'10-SM'14) received his PhD in ECE from Ohio State University in 2010.He is a Professor in the EECS Department at Khalifa University, UAE. His research interests are applications of control and estimation theory on power systems stability, micro and smart grids, renewable energy systems and integration, and process control. He has one US patent, one edited book, 12 book chapters, and over 210 scientific articles in top-tier journals and refereed international conference proceedings. He has supervised/co-supervised

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“…Techniques that use Long Short-Term Memory (LSTM), a time-series analysis method for weather data [39,40], as well as techniques that use both the past and current weather data, have also been proposed. Other studies introduce methods that use the adaptive linear time series model, and a technique for applying both past data and forecasts to the fuzzy decision tree model [41][42][43].…”

Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

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The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

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Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Cited by 20 publications

References 41 publications

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

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