- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 IEEE Congress on Evolutionary Computation (CEC) - Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models
摘要： Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.
关键词： solar forecasting,short-term forecasting,multigene genetic programming,intra-day forecasting
更新于2025-09-23 15:22:29
-
The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
摘要： The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
关键词： NARX neural network,photovoltaic (PV) power forecasting,multisite modelling,short-term forecasting,smart persistence model,classification methods
更新于2025-09-23 15:19:57
-
[IEEE 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Harbin, China (2019.8.11-2019.8.14)] 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Power Forecasting of Photovoltaic Generation Based on Multiple Linear Regression Method with Real-time Correction Term
摘要： This paper proposes a photovoltaic power generation forecasting model which improves Multiple Linear Regression method (MLRM) with real-time correction term traditional day-ahead, hourly power (RCT). Firstly, a generation prediction model is developed by MLRM based on qualitative variables (hour, month, weather type), quantitative variable (solar radiation intensity) and physical characteristics of interactions between the variables. Secondly, an improved is presented which adds a model named MLRM+RCT correction term based on shorter real-time measured power data to MLRM to reduce the hourly prediction errors of MLRM. MLRM+RCT is tested based on power generation data released by IEEE Energy Forecasting Group in 2014. The results show that the performance of MLRM+RCT is better than that of MLRM and a benchmark method called exponential smoothing method.
关键词： Photovoltaic system,real-time correction term,Multiple Linear Regression method,short-term forecasting
更新于2025-09-19 17:13:59