Geometric Particle Swarm Optimization and Reservoir Computing for Solar Power Forecasting

2017 ◽  
pp. 88-97 ◽  
Author(s):  
Sebastián Basterrech
Keyword(s):  
Particle Swarm Optimization ◽  
Solar Power ◽  
Particle Swarm ◽  
Reservoir Computing ◽  
Swarm Optimization ◽  
Solar Power Forecasting ◽  
Power Forecasting

Developing a Decision Model Based on Decision Tree and Particle Swarm Optimization Algorithms to Identify Optimal Locations for Solar Power Plants Construction in Iran

2021 ◽  
Author(s):  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samani ◽  
Foad Minaei ◽  
Hamzeh Karimi Firozjaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Decision Model ◽  
Power Plants ◽  
Solar Power ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Based ◽  
Solar Power Plants

Developing a decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran

2022 ◽  
Author(s):  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Hamzeh Karimi Firozjaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Decision Model ◽  
Power Plants ◽  
Solar Power ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Based ◽  
Solar Power Plants

scholarly journals 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)

2019 ◽  
Vol 9 (16) ◽  
pp. 3214 ◽  
Author(s):  
Nor Azliana Abdullah ◽  
Nasrudin Abd Rahim ◽  
Chin Kim Gan ◽  
Noriah Nor Adzman
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Fuzzy Inference System ◽  
Solar Power ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Inference System ◽  
Neuro Fuzzy

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.

scholarly journals Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

2021 ◽  
Vol 15 (2) ◽  
pp. 111-138
Author(s):  
Pavan Kumar Singh ◽  
Nitin Singh ◽  
Richa Negi
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Smart Grids ◽  
Likelihood Function ◽  
Arima Model ◽  
Particle Swarm ◽  
Model Estimation ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Power Forecasting

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

scholarly journals Optimal Design of Grid-Connected Solar Photovoltaic System Using Selective Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Habtemariam Aberie Kefale ◽  
Elias Mandefro Getie ◽  
Kassaye Gizaw Eshetie
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Solar Power ◽  
Distribution Network ◽  
Particle Swarm ◽  
Network Capacity ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Swarm Optimization ◽  
Solar Photovoltaic System

The electricity distribution network in Ethiopia has the radial nature of network configuration. The interruption of power is due to overloading and failure of distribution lines due to external forces, like trees, animals, and wind. The failure of the radial distribution network brings blackout in the whole power system network as there is no alternative electricity supply. The renewable energy potential of Bahir Dar, Ethiopia, especially solar power is abundant and needs a mechanism to give a response for electricity demand in the country and city other than expecting from the national grid. The solar photovoltaic system interconnection in radial feeders may bring a solution for power interruption and network performance. The sizing and siting of the solar photovoltaic system in the Ethiopian radial distribution system required an optimization tool to obtain better distribution network parameter. The power loss minimization and voltage profile enhancement of the radial distribution network are the key objectives of this research. Selective particle swarm optimization (SPSO) is used to fix the size and site of installation for network capacity enhancement. A multiobjective optimization problem is formulated so as to meet different constraints to be optimized by the SPSO. Finally, the SPSO enables determining proper size and site of solar power installation and bringing better performance in the radial distribution network of Ethiopia.

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