scholarly journals Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine

2021 ◽  
Author(s):  
Lucas Barth Silva ◽  
Roberto Zanetti Freire ◽  
Osíris Canciglieri Junior
Keyword(s):  
Wavelet Transform ◽  
Learning Algorithm ◽  
Spot Markets ◽  
Energy Prices ◽  
The Sustainable Development ◽  
The Social ◽  
Input Variables ◽  
Learning Machine ◽  
Social Importance ◽  
The Garch Model

Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a computational intelligence model using Wavelet Transform on input values and the Extreme Machine Learning algorithm for training and prediction (W-ELM). The macro parameters were optimized using the Particle Swarm Optimization algorithm and for the selection of the input variables, a model based on Mutual Information (MI) was used. In the end, the methodology was compared with the traditional methods: Autoregressive Moving Averages (ARIMA) and General Autoregressive Conditional Heteroskedasticity (GARCH) models. Results showed that the W-ELM had better performance for forecasting 1 to 4 weeks of when compared to ARIMA. When the GARCH model results were considered, the proposed method provided worse performance only for 1 step ahead forecasting.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

scholarly journals Accuracy assessment of extreme learning machine in predicting soil compression coefficient

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Hai-Bang Ly ◽  
Panagiotis G. Asteris ◽  
Thai Binh Pham
Keyword(s):  
Monte Carlo ◽  
Learning Algorithm ◽  
Accuracy Assessment ◽  
Absolute Error ◽  
Training Dataset ◽  
Compression Coefficient ◽  
Input Variables ◽  
Learning Machine ◽  
Soil Compression ◽  
Development And Validation

The compression coefficient (Cc) is an important soil mechanical parameter that represents soil compressibility in the process of consolidation. In this study, a machine learning derived model, namely extreme learning algorithm (ELM), was used to predict the Cc of soil. A total of 189 experimental results were used and randomly divided to construct the training and testing parts for the development and validation of ELM. Monte Carlo approach was applied to take into account the random sampling of samples constituting the training dataset. A number of 13 input parameters reflecting the experiment were used as the input variables to predict the output Cc. Several statistical criteria, such as mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R) and the Monte Carlo convergence estimator were used to assess the performance of ELM in predicting the Cc of soil. The results showed that ELM had a strong capacity to predict the Cc of soil, with the R value > 0.95. The convergence of results, as well as the capability of ELM were fully investigated to understand the advantage of using ELM as a predictor.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Approaches to Supporting Local and Community Development

2018 ◽  
Author(s):  
Liesel Mack Filgueiras ◽  
Andreia Rabetim ◽  
Isabel Aché Pillar
Keyword(s):  
Large Scale ◽  
Mining Company ◽  
The Sustainable Development ◽  
Social Partnership ◽  
The Public ◽  
Mining Investment ◽  
The Social ◽  
Corporate Social

Reflection about the role of community engagement and corporate social investment in Brazil, associated with the presence of a large economic enterprise, is the major stimulus of this chapter. It seeks to present how cross-sector governance can contribute to the social development of a city and how this process can be led by a partnership comprising a corporate foundation, government, and civil society. The concept of the public–private social partnership (PPSP) is explored: a strategy for building a series of inter-sectoral alliances aimed at promoting the sustainable development of territories where the company has large-scale enterprises, through joint efforts towards integrated long-term strategic planning, around a common agenda. To this end, the case of Canaã dos Carajás is introduced, a municipality in the State of Pará, in the Amazon region, where large-scale mining investment is being carried out by the mining company Vale SA.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Toward Sociocultural Indicators of Sustainable Healthy Diets

2021 ◽  
Vol 13 (13) ◽  
pp. 7226
Author(s):  
Jill Nicholls ◽  
Adam Drewnowski
Keyword(s):  
Sustainable Development ◽  
Food Systems ◽  
Research Priorities ◽  
Social Dimension ◽  
The Sustainable Development ◽  
Four Dimensions ◽  
Sustainable Diets ◽  
The Social ◽  
Healthy Diets ◽  
Development Goals

Balancing the social, economic and environmental priorities for public health is at the core of the United Nations (UN) approaches to sustainable development, including the Sustainable Development Goals (SDGs). The four dimensions of sustainable diets are often presented as health, society, economics, and the environment. Although sustainable diet research has focused on health and the environment, the social and economic dimensions of sustainable diets and food systems should not be forgotten. Some research priorities and sociocultural indicators for sustainable healthy diets and food systems are outlined in this report. The present goal is to improve integration of the social dimension into research on food and nutrition security.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

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