Prediction Model for PV Performance With Correlation Analysis of Environmental Variables

Kim, Gyu Gwang; Choi, Jinho; Park, So Young; Bhang, Byeong Gwan; Nam, Woo Jun; Lim, Hae Gyun; Park, Neungsoo; Ahn, Hyungkeun

doi:10.1109/jphotov.2019.2898521

Cited by 117 publications

(68 citation statements)

References 19 publications

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“…The data concerning the insolation of the target area for the last five years and the monthly average power usage of the households in the previous year were collected. Once the PV module and inverter were selected, then, with the simulations from the SolarPro Program and/or modeling method [21], the average power generation per day could be calculated with different environmental conditions. By combining these data with the application and correlation coefficient between the PVs and ESSs, the optimized capacities of the PVs and ESSs can be derived [22].…”

Section: Area Data Of Energy Self-sufficient Householdsmentioning

confidence: 99%

Convergence Analysis of Capacities for Photovoltaics and Energy Storage System Considering Energy Self-Sufficiency Rates and Load Patterns of Rural Areas

et al. 2019

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This work is a case study of 905 households, to present methods for optimizing the capacity of photovoltaic sytems (PVs)/energy storage systems (ESSs) for household to reach a desired energy self-sufficiency (70% to 90%). In order to calculate the capacity of PV and ESSs that would enhance the energy self-sufficiency of families in rural areas, the solar radiance data of the target area for the last five years and the average monthly power usage in the previous year were collected. Then, households with an average energy consumption of 250 kWh per month were chosen for this research. According to the simulation done using Solar Pro, the optimized capacities of PVs and ESSs are 2.67 kW and 7.15 kW, respectively, in order to achieve 90% energy self-sufficiency. We visualized the change in the optimum capacity of PVs and ESSs for the desired energy self-sufficiency. This study would be the base work for forming a grid-distributed energy network system by expanding the system to a national scale.

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Section: Area Data Of Energy Self-sufficient Householdsmentioning

confidence: 99%

Convergence Analysis of Capacities for Photovoltaics and Energy Storage System Considering Energy Self-Sufficiency Rates and Load Patterns of Rural Areas

et al. 2019

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“…E(t) is received from the output. (1) In (1), M is forecasted values to calculate error, E A is actual value and E F is forecasted value corresponding to actual value [18].…”

Section: B Training Patterns For Annmentioning

confidence: 99%

Electric Forecasting using Nature Inspired Optimization Techniques

Singh*,

Srivastava,

Singh

2019

IJEAT

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Now a day, there exists huge competition among power industries in terms of fulfilling various customers' electrical needs. Reliable and quality power supply is no doubt a basic need for all power consumers. Moreover, planning & operation engineers also targets for proper unit commitment, economic power dispatch, etc., and highly depend upon good power system planning. Therefore, Electric Forecasting (EF) is a major criterion for power engineers. In this manuscript, Artificial Neural Network (ANN), being a well established tool for modeling non-linear and black box systems, is used to forecast hydro generation power plant, energy met and peak demand of India.Furthermore, in this competitive world, ANN model is further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) to explore accurate forecasting model with minimal amount of error. These optimization methods explore highly diversified search area, resulting in more accurate forecasting results in comparison to ANN when trained with standard back propagation training algorithm. India. His research area includes power system planning, transformer, energy efficiency, load forecasting, neural network. Navneet Kumar Singh received the PhD degree from Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. Currently he is with Electrical Engineering Department, MNNIT Allahabad, India. His research area includes load forecasting, power system planning and Application of Artificial Intelligence (AI) techniques in power system.

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“…However, they strongly rely on the accuracy of measurements or numerical weather prediction [14]. It should be noted that, meteorological monitoring system could not reflect several important characteristics of PV station (such as tilt angle, panel direction changing by solar tracking control system, and the shadow effect of trees or buildings on each PV array [9]). Furthermore, meteorological factors are difficult to consider comprehensive and the conventional algorithms predicted by using the meteorological data is difficult to avoid the bias accumulation issue which caused by the inaccuracy of on-site collection or meteorological forecasting system.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction method of PV output power based on optimised chaotic phase space reconstruction

Wang

Xue

2020

IET Renewable Power Generation

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With a large number of photovoltaic (PV) generation systems connected to power grid, accurate forecasting becomes important, the results can be used to alleviate their impacts on the grid effectively. However, most of existing methods strongly rely on the numerical weather prediction (NWP), accuracy of them under highly volatile weather conditions is poor. In this study, without using meteorological data, an improved prediction method based on optimised chaotic phase space reconstruction is presented. Firstly, chaos theory is introduced to analyse the evolution law of PV output power. Then, in order to decrease the delay effect of original chaotic attractor and the negative effect of each phase space power sequence's fluctuation on forecasting accuracy, the algorithm of ensemble empirical mode decomposition (EEMD) is introduced to perform further analysis, so as to raise the regularity of chaotic attractors and extract the partial fluctuation features. Finally, based on optimized chaotic attractor and genetic algorithm-back propagation (GA-BP) neural network, the authors build a combined prediction model and apply it into actual measurement data to verify its validation. Numerical results show that by carrying out the optimised chaotic phase space reconstruction, proposed prediction approach achieves better accuracy than the chaos-GA-BP, EEMD-GA-BP and NWP-GA-BP methods.

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Prediction Model for PV Performance With Correlation Analysis of Environmental Variables

Cited by 117 publications

References 19 publications

Convergence Analysis of Capacities for Photovoltaics and Energy Storage System Considering Energy Self-Sufficiency Rates and Load Patterns of Rural Areas

Convergence Analysis of Capacities for Photovoltaics and Energy Storage System Considering Energy Self-Sufficiency Rates and Load Patterns of Rural Areas

Electric Forecasting using Nature Inspired Optimization Techniques

Improved prediction method of PV output power based on optimised chaotic phase space reconstruction

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