Abstract
In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p-power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.
Highlights
In view of the increasing depletion of fossil fuels and the environmental pollution caused by them, every country is sparing no effort to develop technologies for renewable energy power generation
After having considered a wind power prediction error distribution to Gaussian distribution [19], based on the extreme learning machine (ELM) before the minimum square error (MSE-) and prediction-based forecast, we introduce the ELM algorithm based on kernel mean p-power error (KMPE) loss [42]
For wind power plants without a Supervisory Control and Data Acquisition (SCADA) system installed, historical wind power data can be obtained from their internal energy management system, and the data of wind speed, wind direction, air temperature, humidity, and other influencing factors can be obtained through sensors installed on the wind measurement tower
Summary
In view of the increasing depletion of fossil fuels and the environmental pollution caused by them, every country is sparing no effort to develop technologies for renewable energy power generation. It only needs to set the number of hidden nodes in the network and does not need to set the input weight matrix of the network nor the deviation of hidden elements It has a fast learning speed and good generalization performance, and has been widely applied for time series prediction [28], short-term load forecasting [29], wind power ramp events prediction [30], and much more [31,32,33,34]. The novel prediction method mainly uses the ELM model, which can solve the problem that the BP neural network needs to set a large number of parameters artificially.
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