scholarly journals PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3530
Author(s):  
Katarzyna Maciejowska ◽  
Bartosz Uniejewski ◽  
Tomasz Serafin
Keyword(s):  
Predictive Accuracy ◽  
Expert Knowledge ◽  
Predictive Ability ◽  
Principal Component ◽  
Absolute Error ◽  
Electricity Prices ◽  
Short Term ◽  
White Test ◽  
The Mean ◽  
Short Term Forecasting

Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly dependent on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use principal component analysis (PCA) to automate the procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows length. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance, and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperforms other forecast averaging schemes.

Short-Term Forecasting of Electricity Prices Using Mixed Models

2017 ◽  
pp. 153-214
Author(s):  
Carolina García-Martos ◽  
Julio Rodríguez ◽  
María Jesús Sánchez
Keyword(s):  
Mixed Models ◽  
Electricity Prices ◽  
Short Term ◽  
Short Term Forecasting

Prediction Model of Serum Lithium Concentrations

2017 ◽  
Vol 51 (03) ◽  
pp. 82-88 ◽  
Author(s):  
Kazunari Yoshida ◽  
Hiroyuki Uchida ◽  
Takefumi Suzuki ◽  
Masahiro Watanabe ◽  
Nariyasu Yoshino ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Compartment Model ◽  
Absolute Error ◽  
Oral Dosage ◽  
Therapeutic Drug ◽  
Suitable Model ◽  
Blood Draw ◽  
The Mean

Abstract Introduction Therapeutic drug monitoring is necessary for lithium, but clinical application of several prediction strategies is still limited because of insufficient predictive accuracy. We herein proposed a suitable model, using creatinine clearance (CLcr)-based lithium clearance (Li-CL). Methods Patients receiving lithium provided the following information: serum lithium and creatinine concentrations, time of blood draw, dosing regimen, concomitant medications, and demographics. Li-CL was calculated as a daily dose per trough concentration for each subject, and the mean of Li-CL/CLcr was used to estimate Li-CL for another 30 subjects. Serum lithium concentrations at the time of sampling were estimated by 1-compartment model with Li-CL, fixed distribution volume (0.79 L/kg), and absorption rate (1.5/hour) in the 30 subjects. Results One hundred thirty-one samples from 82 subjects (44 men; mean±standard deviation age: 51.4±16.0 years; body weight: 64.6±13.8 kg; serum creatinine: 0.78±0.20 mg/dL; dose of lithium: 680.2±289.1 mg/day) were used to develop the pharmacokinetic model. The mean±standard deviation (95% confidence interval) of absolute error was 0.13±0.09 (0.10–0.16) mEq/L. Discussion Serum concentrations of lithium can be predicted from oral dosage with high precision, using our prediction model.

The Forecasting of Short-Term Wind Speed on Wind Farm Based on Phase Space Reconstruction and Neural Network

2012 ◽  
Vol 246-247 ◽  
pp. 496-500
Author(s):  
Ying Ying Su ◽  
Fei Ma ◽  
Hai Yan Zhang ◽  
Zhi Qiang Liao ◽  
Peng Jun
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Absolute Error ◽  
Phase Space Reconstruction ◽  
The Novel ◽  
Short Term ◽  
Sample Data ◽  
The Mean

The forecasting precision of short-term wind speed is not high for its chaos and time-varying. Aimed at the problem, the novel data space is reconstructed with the best embedding dimension and time delay according to the phase space reconstruction. On the basis, neural network (NN) is used as the modeling tool with the novel sample data. Meanwhile, the structure of NN is confirmed compared with the others on the precision. In the end, the model of short-term wind speed is able to be obtained. The results show that the method is available and the Mean absolute error (MAE) is decreased to 16.2% for 2 hours.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 036119812110007
Author(s):  
Seifeldeen Eteifa ◽  
Hesham A. Rakha ◽  
Hoda Eldardiry
Keyword(s):  
Loss Function ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Loss Functions ◽  
Short Term ◽  
Mean Relative Error ◽  
Squared Error ◽  
The Mean ◽  
Long Short Term Memory

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

scholarly journals Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4046
Author(s):  
Andrei M. Tudose ◽  
Irina I. Picioroaga ◽  
Dorian O. Sidea ◽  
Constantin Bulac ◽  
Valentin A. Boicea
Keyword(s):  
Power Systems ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Linear Regression Method ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
The Mean ◽  
The Impact

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

scholarly journals Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252147
Author(s):  
Ghufran Ahmad ◽  
Furqan Ahmed ◽  
Muhammad Suhail Rizwan ◽  
Javed Muhammad ◽  
Syeda Hira Fatima ◽  
...  
Keyword(s):  
Moving Average ◽  
Absolute Error ◽  
Data Availability ◽  
Data Driven ◽  
Percentage Error ◽  
Pictorial Representation ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Short Term Forecasting

Background The WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries and has been declared a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. Methodology This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 187 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), and random walk forecasts (RWF) with and without drift. For these forecasts, we evaluate the accuracy and systematic errors using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), respectively. Findings The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generate heat maps to provide a pictorial representation of the countries at risk of having an increase in the cases in the coming 4 weeks of February 2021. Conclusion Due to limited data availability during the ongoing pandemic, less data-hungry short-term forecasting models, like ARIMA and ETS, can help in anticipating the future outbreaks of SARS-CoV2.

scholarly journals The Forecast of COVID-19 Spread Risk at The County Level

2021 ◽  
Author(s):  
Murtadha Hssayeni ◽  
Arjuna Chala ◽  
Roger Dev ◽  
Lili Xu ◽  
Jesse Shaw ◽  
...  
Keyword(s):  
United States ◽  
Short Term Memory ◽  
Absolute Error ◽  
The United States ◽  
Lessons Learned ◽  
County Level ◽  
Short Term ◽  
Mobility Data ◽  
Deep Model ◽  
Short Term Forecasting

Abstract The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People’s social behavior as captured by their mobility data plays a role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreak in the United States. The daily data are fed to a deep model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model prediction and the actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. Lower correlation was reported for the counties with a total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4, and it was decreasing with the decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread where average daily cases decrease with the decrease in retires percentage, and increase with the increase in young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also could also help with early and effective management of possible future pandemics.

scholarly journals Pharmacogenetic Warfarin Dosing Algorithm in the Russian Population

2019 ◽  
Vol 4 (3) ◽  
pp. 40-44
Author(s):  
T. A. Bairova ◽  
A. Yu. Sambyalova ◽  
L. V. Rychkova ◽  
E. A. Novikova ◽  
F. I. Belyalov ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Predictive Accuracy ◽  
Warfarin Dose ◽  
Absolute Error ◽  
International Normalized Ratio ◽  
Russian Population ◽  
Asian Patients ◽  
Performance Limitation ◽  
The Mean ◽  
Warfarin Dosing

Background. To date, there are many pharmacogenetic algorithms for selecting the dose of warfarin. However, there is very little information about the predictive accuracy of the algorithms. We decided to evaluate the predictive accuracy of the Gage algorithm, using a calculator, located on the web site (http://www.warfarindosing.org) in two ethnic groups (Caucasians and Asians), living in Russia.Aim. To compare the actual warfarin dose (AWD) to the calculated warfarin dose (CWD), using the algorithm in two ethnic groups taking warfarin.Materials and methods. We included 114 patients (66 Caucasians and 48 Asians): the mean age was60.91 ± 12.34 years; 61 (53.51 %) men, and 53 (46.49 %) women. The comparative characteristics of the algorithm were tested using the mean absolute error (MAE) between AWD and CWD, and percentage of patients, whose CWD fell within 20 % of AWD (percentage within 20 %). Genotyping for CYP2C9*2, CYP2C9*3, CYP4F*2 and VKORC1 was performed by real-time polymerase chain reaction (RT-PCR) method using Pharmacogenetics Warfarin reagent kits (DNA technology, Russia).Results. The Gage algorithm produced the predictive accuracy with MAE = 1.02 ± 0.16 mg/day and percentage within 20 % for Asian patients was 39.6 %. We obtained MAE = 1.33 ± 0.16 mg/day and percentage within 20 % for Caucasian patients was 40.9 %. In two ethnic groups (Caucasians and Asians) of the Russian population, overall performance of warfarin pharmacogenetic dosing by the Gage algorithm was similar.Conclusions. Despite the performance limitation of the current warfarin pharmacogenetic dosing Gage algorithm, constant international normalized ratio monitoring is important.

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