Artificial Intelligence in Electricity Market Operations and Management

2011 ◽  
pp. 1821-1840
Author(s):  
Zhao Yang Dong ◽  
Tapan Kumar Saha ◽  
Kit Po Wong
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Time Series ◽  
Electricity Market ◽  
Support Vector ◽  
Market Demand ◽  
Deregulated Electricity Market ◽  
Vector Machines ◽  
Spike Analysis

This chapter introduces advanced techniques such as artificial neural networks, wavelet decomposition, support vector machines, and data-mining techniques in electricity market demand and price forecasts. It argues that various techniques can offer different advantages in providing satisfactory demand and price signal forecast results for a deregulated electricity market, depending on the specific needs in forecasting. Furthermore, the authors hope that an understanding of these techniques and their application will help the reader to form a comprehensive view of electricity market data analysis needs, not only for the traditional time-series based forecast, but also the new correlation-based, price spike analysis.

Artificial Intelligence in Electricity Market Operations and Management

2011 ◽  
pp. 131-154
Author(s):  
Zhao Y. Dong ◽  
Tapan K. Saha ◽  
Kit P. Wong
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Time Series ◽  
Electricity Market ◽  
Support Vector ◽  
Market Demand ◽  
Deregulated Electricity Market ◽  
Vector Machines ◽  
Spike Analysis

This chapter introduces advanced techniques such as artificial neural networks, wavelet decomposition, support vector machines, and data-mining techniques in electricity market demand and price forecasts. It argues that various techniques can offer different advantages in providing satisfactory demand and price signal forecast results for a deregulated electricity market, depending on the specific needs in forecasting. Furthermore, the authors hope that an understanding of these techniques and their application will help the reader to form a comprehensive view of electricity market data analysis needs, not only for the traditional time-series based forecast, but also the new correlation-based, price spike analysis.

Predicting the Success of Ensemble Algorithms in the Banking Sector

2019 ◽  
Vol 6 (4) ◽  
pp. 12-31
Author(s):  
Özge Hüsniye Namlı Dağ
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Banking Sector ◽  
Hybrid Methods ◽  
Support Vector ◽  
Vector Machines

The banking sector, like other service sector, improves in accordance with the customer's needs. Therefore, to know the needs of customers and to predict customer behaviors are very important for competition in the banking sector. Data mining uncovers relationships and hidden patterns in large data sets. Classification algorithms, one of the applications of data mining, is used very effectively in decision making. In this study, the c4.5 algorithm, a decision trees algorithm widely used in classification problems, is used in an integrated way with the ensemble machine learning methods in order to increase the efficiency of the algorithms. Data obtained via direct marketing campaigns from Portugal Banks was used to classify whether customers have term deposit accounts or not. Artificial Neural Networks and Support Vector Machines as Traditional Artificial Intelligence Methods and Bagging-C4.5 and Boosted-C.45 as ensemble-decision tree hybrid methods were used in classification. Bagging-C4.5 as ensemble-decision tree algorithm achieved more powerful classification success than other used algorithms. The ensemble-decision tree hybrid methods give better results than artificial neural networks and support vector machines as traditional artificial intelligence methods for this study.

scholarly journals Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Peško ◽  
Vladimir Mučenski ◽  
Miloš Šešlija ◽  
Nebojša Radović ◽  
Aleksandra Vujkov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Construction Projects ◽  
Percentage Error ◽  
Support Vector ◽  
Building Process ◽  
Vector Machines ◽  
Specific Part

Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

scholarly journals Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jasleen Kaur ◽  
Khushdeep Dharni
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square ◽  
Data Mining Techniques ◽  
Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

Fundamentals of Machine Learning

2020 ◽  
Author(s):  
Floris Ernst ◽  
Achim Schweikard
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Linear Programming ◽  
Natural Sciences ◽  
Support Vector ◽  
Private Lives ◽  
Vector Machines ◽  
And Mathematics ◽  
Smo Algorithm

Artificial intelligence will change our lives forever - both at work and in our private lives. But how exactly does machine learning work? Two professors from Lübeck explore this question. In their English textbook they teach the necessary basics for the use of Support Vector Machines, for example, by explaining linear programming, the Lagrange multiplier, kernels and the SMO algorithm. They also deal with neural networks, evolutionary algorithms and Bayesian networks. Definitions are highlighted in the book and tasks invite readers to actively participate. The textbook is aimed at students of computer science, engineering and natural sciences, especially in the fields of robotics, artificial intelligence and mathematics.

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