scholarly journals Wind energy forecasts in calculation of expected energy not served

2021 ◽  
Author(s):  
Richard Sun
Keyword(s):  
Markov Chain ◽  
Wind Energy ◽  
Optimal Power Flow ◽  
Markov Chain Model ◽  
Moving Average ◽  
Closed Form Solution ◽  
Arma Model ◽  
Accurate Method ◽  
Form Solution ◽  
Chain Model

The stochastic nature of wind energy generation introduces uncertainties and risk in generation schedules computed using optimal power flow (OPF). This risk is quantified as expected energy not served (EENS) and computed via an error distribution found for each hourly forecast. This thesis produces an accurate method of estimating EENS that is also suitable for real-time OPF calculation. This thesis examines two statistical predictive models used to forecast hourly production of wind energy generators (WEGs), Markov chain model, and auto-regressive moving-average (ARMA) model, and their effects on EENS. Persistence model is used as a benchmark for comparison. For persistence and ARMA models, both Gaussian and Cauchy error distributions are used to compute EENS via a closed-form solution that reduces computational complexity. Markov chain and ARMA both provide accurate forecasts of WEG power generation though Markov Chain model performs significantly better. The Markov chain model also produces the most accurate EENS estimate of the three models.

scholarly journals Wind energy forecasts in calculation of expected energy not served

2021 ◽  
Author(s):  
Richard Sun
Keyword(s):  
Markov Chain ◽  
Wind Energy ◽  
Optimal Power Flow ◽  
Markov Chain Model ◽  
Moving Average ◽  
Closed Form Solution ◽  
Arma Model ◽  
Accurate Method ◽  
Form Solution ◽  
Chain Model

The stochastic nature of wind energy generation introduces uncertainties and risk in generation schedules computed using optimal power flow (OPF). This risk is quantified as expected energy not served (EENS) and computed via an error distribution found for each hourly forecast. This thesis produces an accurate method of estimating EENS that is also suitable for real-time OPF calculation. This thesis examines two statistical predictive models used to forecast hourly production of wind energy generators (WEGs), Markov chain model, and auto-regressive moving-average (ARMA) model, and their effects on EENS. Persistence model is used as a benchmark for comparison. For persistence and ARMA models, both Gaussian and Cauchy error distributions are used to compute EENS via a closed-form solution that reduces computational complexity. Markov chain and ARMA both provide accurate forecasts of WEG power generation though Markov Chain model performs significantly better. The Markov chain model also produces the most accurate EENS estimate of the three models.

scholarly journals Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in Nigeria

2020 ◽  
Vol 9 (3) ◽  
pp. 13
Author(s):  
Phillips Edomwonyi Obasohan
Keyword(s):  
Markov Chain ◽  
Mortality Rate ◽  
Markov Chain Model ◽  
Moving Average ◽  
Arima Model ◽  
Arma Model ◽  
Policy Formulation ◽  
Childhood Mortality ◽  
Chain Model ◽  
Autoregressive Integrated Moving Average

In developing countries, childhood mortality rates are not only affected by socioeconomic, demographic, and health variables, but also vary across regions. Correctly predicting childhood mortality rate trends can provide a clearer understanding for health policy formulation to reduce mortality. This paper describes and compares two prediction methods: Weighted Markov Chain Model (WMC) and Autoregressive Integrated Moving Average (ARIMA) in order to establish which method can better predict the annual child mortality rate in Nigeria. The data for the study were Childhood Mortality Annual Closing Rates (CMACR) data for Nigeria from 1964-2017. The CMACR provides random values changing over time (annually), so we can analyze the mortality closing rate and predict the change range in the next state. Weighted Markov Chain (WMC), a method based on Markov theory, addresses the state and its transition procedures to describe a changing random time series. While the Autoregressive Integrated Moving Average (ARIMA) is a generalization of an Autoregressive Moving Average (ARMA) model. The findings indicate that the ARIMA model predicts CMACR for Nigeria better than WMC. The WMC entered in a loop after two iterations, and we could not use it effectively to predict the future values of CMACR.

A NEW METHOD FOR OBTAINING THE AUTOCOVARIANCE OF AN ARMA MODEL: AN EXACT FORM SOLUTION

1998 ◽  
Vol 14 (5) ◽  
pp. 622-640 ◽  
Author(s):  
M. Karanasos
Keyword(s):  
Moving Average ◽  
Closed Form Solution ◽  
Arma Model ◽  
Form Solution ◽  
New Method ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Model Redundancy

In this article we present a new method for computing the theoretical autocovariance function of an autoregressive moving average model. The importance of our theorem is that it yields two interesting results: First, a closed-form solution is derived in terms of the roots of the autoregressive polynomial and the parameters of the moving average part. Second, a sufficient condition for the lack of model redundancy is obtained.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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