Abstract
With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.
Highlights
With the fast-growing consumption of fossil fuels and the resultant environmental deterioration, we are much encouraged to use renewable energy such as solar energy, wind energy, etc. [1]
The simulation results showed that the FOS-extreme learning machine (ELM) model can improve the accuracy and reduce the training time
Model 1 (FOS-ELM Algorithm): The sigmoid function was chosen as the active function; the data data obtained at 48 h ahead of the current time was used as the obtained at 48 h ahead of the current time was used as the training training data to predict the power output; the training sample was updated every one hour and data to predict the power output; the training sample was updated every one hour and the data the data obtained 48 h ago was dropped
Summary
With the fast-growing consumption of fossil fuels and the resultant environmental deterioration, we are much encouraged to use renewable energy such as solar energy, wind energy, etc. [1]. Sci. 2017, 7, 423 a statistical model based on some machine learning algorithms, and predicts the PV power output directly without building a specific physical model. Some researchers combined the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines (SVM) method [13] These models are proved to be effective in PV prediction, many of them, such as SVM and RBFNN models, are complicated in terms of computation and very data-intensive for the network. In order to achieve this goal, we built the prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which predicts the PV power output for the 15 min in a rolling manner. The simulation results showed that the FOS-ELM model can improve the accuracy and reduce the training time
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