Wavelet ANN Based Monthly Runoff Forecast

2013 ◽  
Vol 421 ◽  
pp. 803-807
Author(s):  
Hui Jun Xu
Keyword(s):  
Neural Network ◽  
Jiangxi Province ◽  
Good Precision ◽  
Hydro Power ◽  
Runoff Series ◽  
Inflow Forecasting ◽  
Runoff Forecast ◽  
Monthly Runoff ◽  
Different Time Scales

A wavelet artificial neural network to forecasting monthly runoff is proposed. The monthly runoff series is firstly decomposed to sub-series on different time scales, and each sub-series is modeled. The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003). Finally, some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.

Minimum Input Variances for Modelling Rainfall-runoff Using ANN

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Zulkarnain Hassan ◽  
Supiah Shamsudin ◽  
Sobri Harun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conceptual Models ◽  
Data Series ◽  
Rainfall Runoff ◽  
Antecedent Rainfall ◽  
Runoff Series ◽  
Artificial Neural Network Ann ◽  
The Relationship

This paper presents the study of possible input variances for modeling the long-term runoff series using artificial neural network (ANN). ANN has the ability to derive the relationship between the inputs and outputs of a process without the physics being provided to it, and it is believed to be more flexible to be used compared to the conceptual models [1]. Data series from the Kurau River sub-catchment was applied to build the ANN networks and the model was calibrated using the input of rainfall, antecedent rainfall, temperature, antecedent temperature and antecedent runoff. In addition, the results were compared with the conceptual model, named IHACRES. The study reveal that ANN and IHACRES can simulate well for mean runoff but ANN gives a remarkable performance compared to IHACRES, if the model customizes with a good configuration.  

Long-term runoff forecast using BP neural network based on climatic factors and mutual information method in the Qujiang Catchment of China

2020 ◽  
Author(s):  
Huihui Dai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Mutual Information ◽  
Bp Neural Network ◽  
Climatic Factors ◽  
Principal Component ◽  
Component Analysis ◽  
Information Method ◽  
Runoff Forecast

<p>The formation of runoff is extremely complicated, and it is not good enough to forecast the future runoff only by using the previous runoff or meteorological data. In order to improve the forecast precision of the medium and long-term runoff forecast model, a set of forecast factor group is selected from meteorological factors, such as rainfall, temperature, air pressure and the circulation factors released by the National Meteorological Center  using the method of mutual information and principal component analysis respectively. Results of the forecast in the Qujiang Catchment suggest the climatic factor-based BP neural network hydrological forecasting model has a better forecasting effect using the mutual information method than using the principal component analysis method.</p>

ARTIFICIAL NEURAL NETWORK FOR LONG TERM LOAD FORECASTING IN UTTARAKHAND STATE: A CASE STUDY

2021 ◽  
pp. 55-56
Author(s):  
Rakesh Kumar ◽  
Rakesh Ranjan ◽  
Mukesh Verma
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
New Technologies ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Hydro Power ◽  
Charging Infrastructure ◽  
The Government ◽  
New Infrastructure

Electricity is one of the essential part of our life. With the increase in consumption of resources the demand of electricity is also increased. Uttarakhand as hilly state is approaching towards implementation of new Technologies and Techniques in the area of growth and suistainable development. Due to the implementation of better road infrastructure, tourism connectivity and IoT devices in various projects and inclusion of electric vehicles and their charging infrastructure in Uttarakhand State the demand of electricity has also increased. The Uttarakhand State has planned the establishment of new infrastructure by providing relaxation on various taxes and option of subsidy to investors. The exemption on xed electricity charges is provided to investors in Uttarakhand. The highest part of Electricity Generation is based on Hydro Power in Uttarakhand. By establishment of new infrastructure in Uttarakhand it would be a thrust to load generation companies to produce demanded of electricity on time. In this study the long-term load forecasting from 2022 to 2030 is analysed using Articial Neural Network. The input data is received from Uttarakhand Electricity Regulatory Commission and Uttarakhand Power Corporation Limited. The prediction is based on last 10 years data of historical load, GDP, Population, and past two years data of electric vehicles, and charging infrastructure. In this study, it has reported that by 2030 there would be huge change in infrastructure and most of diesel and petrol vehicles would come on electric vehicles. This study is focused on the Long-Term Load Forecasting in Uttarakhand State where electric vehicles and charging infrastructure load requirement is also calculated. Using Deep Learning Technique in this paper Articial Neural Network is used for forecasting the results. This tool is used to identify the consumption pattern of electricity in Uttarakhand State for further nine years from 2022 to 2030. The Government of Uttarakhand has planned Vision 2030 for the sustainable development in Uttarakhand.

scholarly journals New fuzzy neural network–Markov model and application in mid- to long-term runoff forecast

2016 ◽  
Vol 61 (6) ◽  
pp. 1157-1169 ◽  
Author(s):  
Biao Shi ◽  
Chang Hua Hu ◽  
Xin Hua Yu ◽  
Xiao Xiang Hu
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Runoff Forecast

A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3390
Author(s):  
Zhanxing Xu ◽  
Jianzhong Zhou ◽  
Li Mo ◽  
Benjun Jia ◽  
Yuqi Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Performance ◽  
Performance Model ◽  
Forecasting Model ◽  
Time Frequency ◽  
Runoff Forecasting ◽  
Proposed Model ◽  
Runoff Series ◽  
Parallel Input ◽  
Monthly Runoff

Runoff forecasting is of great importance for flood mitigation and power generation plan preparation. To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this research has developed a framework of runoff forecasting named Decomposition-Integration-Prediction (DIP) using parallel-input neural network, and proposed a novel runoff forecasting model with Variational Mode Decomposition (VMD), Gated Recurrent Unit (GRU), and Stochastic Fractal Search (SFS) algorithm under this framework. In this model, the observed runoff series is first decomposed into several sub-series via the VMD method to extract different frequency information. Secondly, the parallel layers in the parallel-input neural network based on GRU are trained to receive the input samples of each subcomponent and integrate their output adaptively through the concatenation layers. Finally, the output of concatenation layers is treated as the final runoff forecasting result. In this process, the SFS algorithm was adopted to optimize the structure of the neural network. The prediction performance of the proposed model was evaluated using the historical monthly runoff data at Pingshan and Yichang hydrological stations in the Upper Yangtze River Basin of China, and seven various single and decomposition-based hybrid models were developed for comparison. The results show that the proposed model has obvious advantages in overall prediction performance, model training time, and multi-step-ahead prediction compared to several comparative methods, which is a reasonable and more efficient monthly runoff forecasting method based on time series decomposition and neural networks.

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