scholarly journals A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Deng Yongsheng ◽  
Jiao Fengshun ◽  
Zhang Jie ◽  
Li Zhikeng
Keyword(s):  
Correlation Analysis ◽  
Power Output ◽  
Short Term Memory ◽  
Rank Order ◽  
Influence Factors ◽  
Forecasting Model ◽  
Short Term ◽  
Pv System ◽  
Factors Affecting ◽  
Model Based

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.

The importance of short lag-time in the runoff forecasting model based on long short-term memory

2020 ◽  
Vol 589 ◽  
pp. 125359
Author(s):  
Xi Chen ◽  
Jiaxu Huang ◽  
Zhen Han ◽  
Hongkai Gao ◽  
Min Liu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Lag Time ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Forecasting Model of Maritime Accidents Based on Influencing Factors Analysis

2012 ◽  
Vol 253-255 ◽  
pp. 1268-1272
Author(s):  
Dong Wang ◽  
Chao Zhong Yin ◽  
Jian Ai
Keyword(s):  
Bp Neural Network ◽  
Network Modeling ◽  
Influence Factors ◽  
Forecast Model ◽  
Neural Network Modeling ◽  
Forecasting Model ◽  
Short Term ◽  
Factors Affecting ◽  
Factors Analysis ◽  
Modeling Process

The factors affecting the maritime accidents are complicated. Digging up the factors and finding out the inherent laws,maritime accidents can be forecast in a short-term and medium-and-long-term.The paper analyzes the factors and discusses the BP neural network modeling process of maritime accidents based on influence factors. Through the validation, the forecast model of maritime accidents is feasible.

scholarly journals Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1856 ◽  
Author(s):  
Munir Husein ◽  
Il-Yop Chung
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Power Output ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Irradiance Data ◽  
Long Short Term Memory

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

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