- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
摘要： The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
关键词： solar irradiation,adaptive neuro-fuzzy inference systems,PVs power output forecasting,particle swarm optimization-artificial neural networks
更新于2025-09-19 17:13:59
-
Solar photovoltaic power output forecasting using machine learning technique
摘要： Photovoltaic (PV) systems are used around the world to generate solar power. Solar power sources are irregular in nature due to the output power of PV systems being intermittent and depending greatly on environmental factors. These factors include, but are not limited to, irradiance, humidity, PV surface temperature, speed of the wind. Due to uncertainties in the photovoltaic generation, it is critical to precisely envisage the solar power generation. Solar power forecasting is necessary for supply and demand planning in an electric grid. This prediction is highly complex and challenging as solar power generation is weather-dependent and uncontrollable. This paper describes the effects of various environmental parameters on the PV system output. Prediction models based on Artificial Neural Networks (ANN) and regression models are evaluated for selective factors. The selection is done by using the correlation-based feature selection (CSF) and ReliefF techniques. The ANN model outperforms all other techniques that were discussed.
关键词： solar photovoltaic,regression models,Artificial Neural Networks,power output forecasting,machine learning
更新于2025-09-19 17:13:59
-
An Ensemble Framework For Day-Ahead Forecast of PV Output in Smart Grids
摘要： The uncertainty associated with solar photo-voltaic (PV) power output is a big challenge to design, manage and implement effective demand response and management strategies. Therefore, an accurate PV power output forecast is an utmost importance to allow seamless integration and a higher level of penetration. In this research, a neural network ensemble (NNE) scheme is proposed, which is based on particle swarm optimization (PSO) trained feedforward neural network (FNN). Five different FFN structures with varying network complexities are used to achieve the diverse and accurate forecast results. These results are combined using trim aggregation after removing the upper and lower forecast error extremes. Correlated variables namely wavelet transformed historical power output of PV, solar irradiance, wind speed, temperature and humidity are applied as inputs to the multivariate NNE. Clearness index is used to classify days into clear, cloudy and partial cloudy days. Test case studies are designed to predict the solar output for these days selected from all seasons. The performance of the proposed framework is analyzed by applying training data set of different resolution, length and quality from seven solar PV sites of the University of Queensland, Australia. The forecast results demonstrate that the proposed framework improves the forecast accuracy significantly in comparison with individual and benchmark models.
关键词： clear day (CD),solar irradiance,cloudy day (CLD),clearness index,PV power output forecasting,ensemble network (EN),neural network ensemble (NNE),partially cloudy day (PCD),particle swarm optimization
更新于2025-09-10 09:29:36