scholarly journals Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 409
Author(s):  
Samer Chaaraoui ◽  
Matthias Bebber ◽  
Stefanie Meilinger ◽  
Silvan Rummeny ◽  
Thorsten Schneiders ◽  
...  
Keyword(s):  
Linear Regression ◽  
Health Facility ◽  
Statistical Approach ◽  
Short Term Memory ◽  
High Reliability ◽  
Moving Average ◽  
Health Sector ◽  
Cost Effective ◽  
System Control ◽  
Electrical Load

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.

scholarly journals Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Bilin Shao ◽  
Maolin Li ◽  
Yu Zhao ◽  
Genqing Bian
Keyword(s):  
Prediction Accuracy ◽  
Short Term Memory ◽  
High Reliability ◽  
Moving Average ◽  
Arima Model ◽  
Pso Algorithm ◽  
Metal Exchange ◽  
Nickel Metal ◽  
Learning Factor ◽  
Improved Pso

Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.

Predictive Analytic for Blood Request by Using Moving Average and Linear Regression

2019 ◽  
Vol 7 (5) ◽  
pp. 1323-1329
Author(s):  
P. Limsaiprom ◽  
S. Rattanachan ◽  
N. Phuangphairoj ◽  
M. Kaenchuwongk
Keyword(s):  
Linear Regression ◽  
Moving Average

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

scholarly journals Empirically evaluating the impact of adjudicative tribunals in the health sector: context, challenges and opportunities

2011 ◽  
Vol 7 (2) ◽  
pp. 147-174
Author(s):  
Steven J. Hoffman ◽  
Lorne Sossin
Keyword(s):  
Health System ◽  
Health Sector ◽  
Cost Effective ◽  
System Level ◽  
Challenges And Opportunities ◽  
External Impact ◽  
Health System Governance ◽  
Evaluation Matrix ◽  
The Impact ◽  
Methodological Difficulties

AbstractAdjudicative tribunals are an integral part of health system governance, yet their real-world impact remains largely unknown. Most assessments focus on internal accountability and use anecdotal methodologies; few, studies if any, empirically evaluate their external impact and use these data to test effectiveness, track performance, inform service improvements and ultimately strengthen health systems. Given that such assessments would yield important benefits and have been conducted successfully in similar settings (e.g. specialist courts), their absence is likely attributable to complexity in the health system, methodological difficulties and the legal environment within which tribunals operate. We suggest practical steps for potential evaluators to conduct empirical impact evaluations along with an evaluation matrix template featuring possible target outcomes and corresponding surrogate endpoints, performance indicators and empirical methodologies. Several system-level strategies for supporting such assessments have also been suggested for academics, health system institutions, health planners and research funders. Action is necessary to ensure that policymakers do not continue operating without evidence but can rather pursue data-driven strategies that are more likely to achieve their health system goals in a cost-effective way.

scholarly journals Two-phased DEA-MLA approach for predicting efficiency of NBA players

2014 ◽  
Vol 24 (3) ◽  
pp. 347-358 ◽  
Author(s):  
Sandro Radovanovic ◽  
Milan Radojicic ◽  
Gordana Savic
Keyword(s):  
Linear Regression ◽  
High Reliability ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Efficiency Frontier ◽  
Function Form ◽  
Vector Machines ◽  
Challenging Tasks ◽  
Dea Analysis

In sports, a calculation of efficiency is considered to be one of the most challenging tasks. In this paper, DEA is used to evaluate an efficiency of the NBA players, based on multiple inputs and multiple outputs. The efficiency is evaluated for 26 NBA players at the guard position based on existing data. However, if we want to generate the efficiency for a new player, we would have to re-conduct the DEA analysis. Therefore, to predict the efficiency of a new player, machine learning algorithms are applied. The DEA results are incorporated as an input for the learning algorithms, defining thereby an efficiency frontier function form with high reliability. In this paper, linear regression, neural network, and support vector machines are used to predict an efficiency frontier. The results have shown that neural networks can predict the efficiency with an error less than 1%, and the linear regression with an error less than 2%.

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

scholarly journals A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

2022 ◽  
pp. 179-186
Author(s):  
Yuvraj Praveen Soni ◽  
Eugene Fernandez
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Solar Irradiance ◽  
Harmonic Distortion ◽  
System Control ◽  
Solar Pv ◽  
Pv System ◽  
Simulink Model ◽  
The Impact

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

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