Short and long-term wind turbine power output prediction

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model

Developing An Automated Forecasting Framework For Predicting Operation Room Block Time

Operating rooms are the most important part of the hospitals, since they have highest influence on financial state of the hospital. Because of high uncertainty in surgery cases demands and their durations, the scheduling of the surgeries becomes a very challenging and critical issue in hospitals. One of the most common approaches to overcome this uncertainty is applying block times which is the time intervals allocated to surgery groups in the hospital. Assigning sufficient amount of the time to each block, is very important, since overestimating lead to wasting resources and on the other hand underestimation causes the overtime staffing and probably surgery cancellation. The objective of this study is developing an automatic forecasting framework with applying a high performance forecasting methods to predict the future block time intervals for surgical groups. The main property of proposed forecasting framework is elimination of the human intervention which means the system follows the certain algorithms to perform the forecasting. In this framework we have applied four different methods include exponential smoothing, ARMA, artificial neural network and hybrid ANN-ARMA methodology, then by applying multi-criteria decision analysis, the most effective method can be selected. The accurate forecasting can result in reductions in total waiting time, idle time, and overtime costs. We illustrate this with results of a case study which conducted by real world data at John D. Dingell Detroit VA Medical Center

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.Electricity, price forecasting, ARMA model, seasonal component

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

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi

A Model for Stock Price Prediction Using the Soft Computing Approach

A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors’ confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts’ opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving average (ARIMA) model which has been widely used for time series forecasting. Our findings revealed that ARIMA models cannot be effectively engaged profitably for stock price prediction. It was also observed that the pattern of ARIMA forecasting models were not satisfactory. The developed stock price predictive model with the ANN-based soft computing approach demonstrated superior performance over the ARIMA models; indeed, the actual and predicted value of the developed stock price predictive model were quite close

Forecasting future energy production using hybrid artificial neural network and arima model

The objective of this research is to obtain an accurate forecasting model for the amount of electricity (in kWh) that is generated from different primary energy sources in the U.S. In this research, Artificial Neural Network (ANN) and hybrid ARIMA and ANN algorithms were developed that can be used for forecasting the amount of energy production in the short, as well as, in the long run. Based on the inferences made from the available data provided by Energy Information Administration from January 2004 to December 2014, two different forecasting models for each primary energy source were constructed. These two models were validated with available data from January 2015 to November 2017, and their performance, as measured by forecasting errors computed, were compared. The results show that ANN algorithm is good for fossil fuels sources such as coal, petroleum, and natural gas. However, ARIMA - ANN hybrid works more accurately for renewable energy sources such as geothermal, hydroelectric, solar, and wind. Finally, the best predictor was selected for each primary energy source which provides valuable information regarding the future electricity generation, and future dominant energy source to generate electricity. This information will hopefully influence energy sector forecasting models and help the government to develop future regulations to shift toward dominant energy sources of the future

Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data

Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting

International audienceIn this work, we introduce a new modeling and inferential tool for dynamical processing of time series. The approach is called recurrent dictionary learning (RDL). The proposed model reads as a linear Gaussian Markovian state-space model involving two linear operators, the state evolution and the observation matrices, that we assumed to be unknown. These two unknown operators (that can be seen interpreted as dictionaries) and the sequence of hidden states are jointly learnt via an expectation-maximization algorithm. The RDL model gathers several advantages, namely online processing, probabilistic inference, and a high model expressiveness which is usually typical of neural networks. RDL is particularly well suited for stock forecasting. Its performance is illustrated on two problems: next day forecasting (regression problem) and next day trading (classification problem), given past stock market observations. Experimental results show that our proposed method excels over state-of-the-art stock analysis models such as CNN-TA, MFNN, and LSTM