A comparative study on ANN techniques in predicting solar radiation for various meteorological locations of Bangladesh

Nandi, Sanjib Kumar; Rahman, Md. Siddikur; Riadh, Ridown Rashid

doi:10.1109/iciev.2016.7760122

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Much research has also been devoted to the forecasting and estimation of solar PV generation or solar irradiance [13]- [25], results of case studies show ANN algorithm can deal with prediction matter and uncertainty well. Besides, solar PV prediction is usually a critical part of supply-demand planning in an electrical grid.…”

Section: Introductionmentioning

confidence: 99%

Application of Parallel ANN-PSO to Hourly Solar PV Estimation

Cho¹,

Lee²,

Chang³

2021

Preprint

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Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.

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Section: Introductionmentioning

confidence: 99%

Application of Parallel ANN-PSO to Hourly Solar PV Estimation

Cho¹,

Lee²,

Chang³

2021

Preprint

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“…Yang et al, 24 proposed a typical MGSR, which is suitable for unmeasurable, relatively concentrated distributions and similar locations. Nandi et al 25 presented a multi‐layeredfeed‐forward ANN structure utilizing the back‐propagation algorithm to determine the monthly average insolation incident on horizontal surfaces. The training data are derived primarily from the NASA database, and the estimated solar irradiance values are compared with the data from the official Meteorological Department to verify the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

Cho

Chang

Huang

2020

Int Trans Electr Energ Syst

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Summary Based on existing power generation data, an hourly area solar power estimation model using the parallel Elman neural network with solar radiation and system conversion efficiency is proposed. The accuracy and reliability of the assessment were verified using the information/data of solar photovoltaic power stations in various regions and timescales. Using the established appraisal algorithm involving K‐means evaluation and inverse distance weighting, regional forecasting of solar power generation was achieved. The prediction accuracy was also investigated using the actual details of the photovoltaic power stations. The results of the proposed model can assist the electricity dispatcher to not only precisely monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet the load demand more accurately. The proposed method can benefit power dispatching involving a larger scale of intermittent and unstable solar power electricity in the future.

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“…The comparison studies between the MLP model with other forecasting strategies were done by previous works. Nandi et al compared the MLP model with the Klucher and isotropic sky models [10]. The outcomes showed that there was a good agreement between the forecasted values and actual values of solar radiation, which represents the outstanding performance of the MLP model.…”

Section: Introductionmentioning

confidence: 99%

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)

et al. 2019

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Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.

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A comparative study on ANN techniques in predicting solar radiation for various meteorological locations of Bangladesh

Cited by 6 publications

References 23 publications

Application of Parallel ANN-PSO to Hourly Solar PV Estimation

Application of Parallel ANN-PSO to Hourly Solar PV Estimation

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

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)

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