6 research outputs found
A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST
Electrical load forecast is an important part of the power system energy management
system. Reliable load forecast technique will help the electric utility to make unit
commitment decisions, reduce spinning reserve capacity, and schedule device
maintenance plan properly. Thus, besides being a key element in reducing the
generation cost, power load forecast is an essential procedure in enhancing the
reliability of the power systems. Generally speaking, power systems worldwide are
using load forecast as an essential part of off-line network analysis. This is in order
to determine the status of the system, and the necessity to implement corrective
actions, such as load shedding, power purchases or using peaking units.
Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and
168-hours ahead is a necessary daily task for power dispatch. Its accuracy will
significantly affect the cost of generation and the reliability of the system. The
majority of the single variable based techniques are using autoregressive-moving
average (ARMA) model to solve the STLF problem.
In this thesis, a new AR algorithm especially designed for long data records as a
solution to STLF problem is proposed. The proposed AR-based algorithm divides
long data record into short segments and searches for the AR coefficients that
simultaneously model the data with the least means squared errors. In order to verify
the proposed algorithm as a solution to STLF problem, its performance is compared
with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA
(SARIMA). In addition to the parametric algorithms, the comparison is extended
towards artificial neural networks (ANN). Three years data power demand record
collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC,
between the beginning of 2005 and the end of 2007 are used for the comparison. The
results show the potential of the proposed algorithm as a reliable solution to STLF
A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST
Electrical load forecast is an important part of the power system energy management
system. Reliable load forecast technique will help the electric utility to make unit
commitment decisions, reduce spinning reserve capacity, and schedule device
maintenance plan properly. Thus, besides being a key element in reducing the
generation cost, power load forecast is an essential procedure in enhancing the
reliability of the power systems. Generally speaking, power systems worldwide are
using load forecast as an essential part of off-line network analysis. This is in order
to determine the status of the system, and the necessity to implement corrective
actions, such as load shedding, power purchases or using peaking units.
Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and
168-hours ahead is a necessary daily task for power dispatch. Its accuracy will
significantly affect the cost of generation and the reliability of the system. The
majority of the single variable based techniques are using autoregressive-moving
average (ARMA) model to solve the STLF problem.
In this thesis, a new AR algorithm especially designed for long data records as a
solution to STLF problem is proposed. The proposed AR-based algorithm divides
long data record into short segments and searches for the AR coefficients that
simultaneously model the data with the least means squared errors. In order to verify
the proposed algorithm as a solution to STLF problem, its performance is compared
with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA
(SARIMA). In addition to the parametric algorithms, the comparison is extended
towards artificial neural networks (ANN). Three years data power demand record
collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC,
between the beginning of 2005 and the end of 2007 are used for the comparison. The
results show the potential of the proposed algorithm as a reliable solution to STLF
ARMAsel for Detection and Correction of Outliers in Univariate Stochastic Data
For stationary random data, an automatic estimation algorithm can now select a time series model with a spectral accuracy close to the Cramér–Rao lower bound. The parameters of that selected time series model accurately represent the spectral density and the autocovariance function of the data. That is all the possible information for Gaussian data, as well as the most important information for arbitrarily distributed data. A single model type and order is selected from many candidate time series models by looking for the smallest prediction error. The single selected model precisely includes only the statistically significant details that are present in the data. The residuals of the automatically selected time series model reveal the location of outliers or other irregularities that may not be visible in the measured signal. The program requires no user interaction and can be incorporated into automatic measurement instruments and protocols.Multi-Scale PhysicsApplied Science
Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements
Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating
the cardiovascular system, and is an integral component of intensive care units, obstetrics
wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective
and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive
methods such as Pulmonary artery catheter or transoesophageal echocardiography.
However, Doppler ultrasound scan acquisition requires a highly experienced operator and
can be very challenging. Machine learning solutions that quantify and guide the scanning
process in an automatic and intelligent manner could overcome these limitations and lead
to routine monitoring. Development of such methods is the primary goal of the presented
work.
In response to this goal, this thesis proposes a suite of signal processing and machine
learning techniques. Among these is a new and real-time method of maximum frequency
envelope estimation. This method, which is based on image-processing techniques and is
highly adaptive to varying signal quality, was developed to facilitate automatic and consistent
extraction of features from Doppler ultrasound measurements. Through a thorough
evaluation, this method was demonstrated to be accurate and more stable than alternative
state-of-art methods.
Two novel real-time methods of beat segmentation, which operate using the maximum
frequency envelope, were developed to enable systematic feature extraction from individual
cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram
machine, and are fully automatic, real-time and highly resilient to noise.
These qualities are not available in existing methods. Extensive evaluation demonstrated
the methods to be highly successful.
A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of-
the-art image recognition classification method, hitherto undocumented for Doppler
ultrasound analysis, was shown to be superior to more traditional modelling approaches.
These contributions facilitated the design of two innovative types of feedback. To reflect
beneficial probe movements, which are otherwise difficult to distinguish, a regression model
to quantitatively score ultrasound measurements was proposed. This feedback was shown
to be highly correlated with an ideal response.
The second type of feedback explicitly predicted beneficial probe movements. This was
achieved using classification models with up to five categories, giving a more challenging
scenario than those addressed in prior disease classification work. Evaluation of these, for
the first time, demonstrated that Doppler scan information can be used to automatically
indicate probe position.
Overall, the presented work includes significant contributions for Doppler ultrasound
analysis, it proposes valuable new machine learning techniques, and with continued work,
could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic
monitoring