A Hidden Markov Model for a Day-Ahead Prediction of Half-Hourly Energy Demand in Romanian Electricity Market

2019 ◽  
pp. 307-317
Author(s):  
Anatoli Paul Ulmeanu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Electricity Market ◽  
Energy Demand ◽  
Hidden Markov

scholarly journals Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Da Liu ◽  
Yanan Wei ◽  
Shuxia Yang ◽  
Zhitao Guan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Electricity Market ◽  
Hidden Markov ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecast ◽  
Adaptive Weights ◽  
Generalized Regression Neural Networks ◽  
The Individual

A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM) is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM), USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM), generalized regression neural networks (GRNN), day-ahead modeling, and self-organized map (SOM) similar days modeling.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.

Hidden Markov Model Classification of Myoelectric Signals in Speech

2001 ◽  
Author(s):  
A. D. Chan ◽  
K. Englehart ◽  
B. Hudgins ◽  
D. F. Lovely
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Myoelectric Signals ◽  
Model Classification
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