A new strategy to quantify uncertainties of wavelet-GRNN-PSO based solar PV power forecasts using bootstrap confidence intervals

AlHakeem, Donna; Mandal, Paras; Haque, Ashraf Ul; Yona, Atsushi; Senjyu, Tomonobu; Tseng, Tzu-Liang

doi:10.1109/pesgm.2015.7286233

Cited by 28 publications

(13 citation statements)

References 18 publications

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“…and machine learning methods (ANN, SVM, ELM etc.). ANN, SVM, ELM methods are commonly adopted in the proposed hybrid models, in conjunction with few optimisation methods such as GA, FFA, CRO, PSO, EA, SFLA, LM and GSO as found in recent articles [112,122,123,125,130,136,137,147]. The function of these optimisation algorithms is to find the best solution for the hybrid method of interest.…”

Section: Comprehensive Comparative Discussion For Machine Learning Anmentioning

confidence: 99%

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Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Akhter

Mekhilef

Mokhlis

et al. 2019

IET Renewable Power Generation

334

147

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The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

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Section: Comprehensive Comparative Discussion For Machine Learning Anmentioning

confidence: 99%

“…NMAE was found 6.72% (1 h forecast) and NRMSE was found to be 16.89% (3 h forecast) and 3.33% (6 h forecast). From the observation table, it was clear that both errors (NRMSE, NMAE) increased with the increase of horizon, hence decreasing the forecasting accuracy as a result [123].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Akhter

Mekhilef

Mokhlis

et al. 2019

IET Renewable Power Generation

334

147

View full text Add to dashboard Cite

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“…Another study by Lee et al 28 used short-term and very short-term day-ahead forecasting of PV-generated energy and developed a scheduling algorithm for an ESS-PV system. AlHakeem et al 66 also used the bootstrap confidence interval method for PV and wind-generated energy forecasting and developed a management scheme for energy storage. Kodaira et al 32 proposed an optimisation algorithm based on probabilistic load prediction for an ESS-PV system for peak shaving.…”

Section: Discussionmentioning

confidence: 99%

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

et al. 2021

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The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV-generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV-generated energy. We propose a novel integrated energy-efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)-based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV-generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL-based forecasting models for PV and EHP are accurate and reliable (ie, R 2 above 0.95). Also, the results show that the proposed integrated energy-efficient PV-ESS-EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV-generated energy resulting in reduced dependency on fossil fuels for building energy needs.

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“…Obtained new positions of each particle must be within specified boundaries. If any component of a particle violates the boundary condition, then that component is set to as (11) shown:…”

Section: Parallel Ann-pso Algorithmmentioning

confidence: 99%

“…The proposed hybrid PSO model has been applied in the area of solar PV forecasting. Test results demonstrate the high degree of efficiency in multiple seasons including sunny days, cloudy days, and rainy days [11]. A parallel PSO method extracted and estimated the parameters of a PV model in relation to traditional I-V characteristic.…”

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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A new strategy to quantify uncertainties of wavelet-GRNN-PSO based solar PV power forecasts using bootstrap confidence intervals

Cited by 28 publications

References 18 publications

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs

Application of Parallel ANN-PSO to Hourly Solar PV Estimation

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