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.

scholarly journals Physical Context Detection using Wearable Wireless Sensor Networks

2008 ◽  
Vol 4 (3) ◽  
pp. 191 ◽  
Author(s):  
Muhannad Quwaider ◽  
Subir Biswas
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Real Life ◽  
Sensor Data ◽  
Physical Context ◽  
Significant Extension ◽  
The Individual ◽  
Stochastic Identification

This paper presents the architecture of a wearable sensor network and a Hidden Markov Model (HMM) processingframework for stochastic identification of body postures andphysical contexts. The key idea is to collect multi-modal sensor data from strategically placed wireless sensors over a human subject’s body segments, and to process that using HMM in order to identify the subject’s instantaneous physical context. The key contribution of the proposed multi-modal approach is a significant extension of traditional uni-modal accelerometry in which only the individual body segment movements, without their relative proximities and orientation modalities, is used for physical context identification. Through real-life experiments with body mounted sensors it is demonstrated that while the unimodal accelerometry can be used for differentiating activityintensive postures such as walking and running, they are not effective for identification and differentiation between lowactivity postures such as sitting, standing, lying down, etc. In the proposed system, three sensor modalities namely acceleration, relative proximity and orientation are used for context identification through Hidden Markov Model (HMM) based stochastic processing. Controlled experiments using human subjects are carried out for evaluating the accuracy of the HMMidentified postures compared to a naïve threshold based mechanism over different human subjects.

Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor-Based Dynamic State Estimation

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Ritika Sibal ◽  
Ding Zhang ◽  
Julie Rocho-Levine ◽  
K. Alex Shorter ◽  
Kira Barton
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
State Estimation ◽  
Short Term Memory ◽  
Hidden Markov ◽  
Training Data ◽  
Dynamic State ◽  
Support Vector ◽  
Similar Amount ◽  
Dynamic State Estimation

Abstract Behavior of animals living in the wild is often studied using visual observations made by trained experts. However, these observations tend to be used to classify behavior during discrete time periods and become more difficult when used to monitor multiple individuals for days or weeks. In this work, we present automatic tools to enable efficient behavior and dynamic state estimation/classification from data collected with animal borne bio-logging tags, without the need for statistical feature engineering. A combined framework of an long short-term memory (LSTM) network and a hidden Markov model (HMM) was developed to exploit sequential temporal information in raw motion data at two levels: within and between windows. Taking a moving window data segmentation approach, LSTM estimates the dynamic state corresponding to each window by parsing the contiguous raw data points within the window. HMM then links all of the individual window estimations and further improves the overall estimation. A case study with bottlenose dolphins was conducted to demonstrate the approach. The combined LSTM–HMM method achieved a 6% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily adaptable to new tasks.

Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs

2017 ◽  
Vol 4 (2) ◽  
pp. 94-98
Author(s):  
ShiJie Zhao ◽  
Toshihiko Sasama ◽  
Takao Kawamura ◽  
Kazunori Sugahara
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sensor Network ◽  
Hidden Markov ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Network System ◽  
Daily Lives

We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors. Index Terms—multifunctional outlet system; behavior detection; hidden markov model; sensor network; support vector machine. REFERENCES [1] T.Sasama, S.Iwasaki, and T.Okamoto, “Sensor Data Classification for Indoor Situation Using the Multifunctional Outlet”, The Institute of Electrinical Engineers of Japan, vol.134(7),2014,pp.949-995 [2] M.Anjali Manikannan, R.Jayarajan, “Wireless Sensor Netwrork For Lonely Elderly Perple Wellness”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, vol. 3, 2015, pp.41-45 [3] Nagender Kumar Suryadevara, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, JUNE 2012, pp. 1965-1972. [4] iTec Co., safety confirmation system: Mimamorou, http://www.minamoro.biz/. [6] Alexander Schliep's group for bioinformatics, The General Hidden Markov Model library(GHMM), http://ghmm.sourceforge.net/. [7] Jr Joe H.Ward, Joumal of the American Statistical Association, vol58(301), 1963, pp236-244 [5] SOLXYZ Co., status monitoring system:Ima-Irumo, http://www.imairumo.com/.

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