In this paper, an extreme learning machine model based on the improved similar day to predict the short-term output of photovoltaic (PV) is presented. The Pearson correlation coefficient is used to analyze the influence of various meteorological factors on the output of photovoltaic power generation, so as to find out the meteorological factors that have a greater impact on photovoltaic output. And then the prediction model is to be established with combining the fuzzy clustering method by improving the similar day selection method. The multi-day photovoltaic output data with the highest correlation about the day to be tested is used to train the extreme learning machine neural network which is then used to predict the PV output of the day to be measured. Finally, the experimental results show that the proposed method has higher prediction accuracy and shorter calculation time than the traditional prediction method, and what's more, the algorithm is simple and the prediction cost is low. It has a wide application value and research space.

Research on Predicting the Short-term Output of Photovoltaic (PV) Based on Extreme Learning Machine Model and Improved Similar Day

To solve the problem of unstable prediction results of traditional power load forecasting methods, a power dynamic load complementary coefficient forecasting method based on big data technology is proposed. The prediction method is mainly divided into the following four parts. The data caliber and density are collected to determine the reasonable data range. Then, according to the big data technology, the curve function of charge movement is calculated to calculate the complementary coefficient, and the complementary coefficient is combined to complete the prediction. The experimental results show that the power dynamic load complementarity coefficient prediction method based on big data technology proposed for the traditional charge prediction method is more stable, so this method is more effective.

Research on Prediction of Dynamic Load Complementarity Based on Big Data

Establishing an Exhaust Gas Temperature (EGT) baseline model is an important way to realize the overall performance real time monitoring and health management of an aeroengine. An EGT baseline model of aeroengine was constructed by combining Kernel Principal Component Analysis (KPCA) with Deep Belief Network (DBN). The Quick Access Recorder (QAR) data of the airplane with CFMS6-7B aeroengine was taken as the original data sample. The data dimensions were first reduced using KCPA, where Gaussian function was selected as Kernel function. The dimensionality-reduced data was put into DBN to establish the EGT baseline model. The validity and superiority of the model were verified by a large number of QAR data. Compared with traditional neural network modeling techniques, the proposed method not only reduces the complexity of the network structure, but also improves the accuracy of the model.

EGT Baseline Model of Aeroengine Based on Kernel Principal Component Analysis and Deep Belief Network

Research on Short-term Power Load Forecasting Method Based on Temperature Accumulation Effect Embedded in Time Series

Accuracy of power load forecasting was researched.Power load is related with weather,economy,and holiday factors.The variation rule is cyclical and random,and traditional method cannot describe the variation rule,which leads to low accuracy of prediction.In order to improve the accuracy of power load forecasting,an extreme learning machine(ELM) was proposed for predicting the power load,by integrating similar days selecting method based on the time sequence encoding.The method that selects similar days based on the time sequence encoding integrates the information of the entire sequence into each encoded point,which can not only describe the trend of a sequence but also can describe the relative position of the point in the sequence.Then,the ELM was used for prediction,because it only need to set the number of hidden layer nodes and can generate a unique optimal solution,while the input weights of the network and the bias of the hidden layer nodes do not need to adjust during the execution of the algorithm.The power load data of one building were used for simulating.The proposed method was compared with support vector machines(SVM) and back propagation(BP) neural network,and the experimental results show that the proposed method has high prediction precision,high adaptability and shorter running time.

Short-Term Power Load Forecasting Based on Extreme Learning Machine

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Short-Term Power Load Forecasting Based on Extreme Learning Machine