Search results for "Mean absolute percentage error"
showing 9 items of 9 documents
Bayesian dynamic modeling of time series of dengue disease case counts
2017
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order …
An efficient data model for energy prediction using wireless sensors
2019
International audience; Energy prediction is in high importance for smart homes and smart cities, since it helps reduce power consumption and provides better energy and cost savings. Many algorithms have been used for predicting energy consumption using data collected from Internet of Things (IoT) devices and wireless sensors. In this paper, we propose a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information (e.g., light energy, day of the week, humidity, temperature, etc.) from a Wireless Sensor Network (WSN). We compare our system against four other classification algorithms, namely: Linear Regression (LR), Support Vector Machin…
A New Fitness Test of Estimating VO2max in Well-Trained Rowing Athletes
2021
BackgroundThis study was designed to investigate the validity of maximal oxygen consumption (VO2max) estimation through the Firstbeat fitness test (FFT) method when using submaximal rowing and running programs for well-trained athletes.MethodsWell-trained flatwater rowers (n = 45, 19.8 ± 3.0 years, 184 ± 8.7 cm, 76 ± 12.9 kg, and 58.7 ± 6.0 mL⋅kg–1⋅min–1) and paddlers (n = 45, 19.0 ± 2.5 years, 180 ± 7.7 cm, 74 ± 9.4 kg, and 59.9 ± 4.8 mL⋅kg–1⋅min–1) completed the FFT and maximal graded exercise test (GXT) programs of rowing and running, respectively. The estimated VO2max was calculated using the FFT system, and the measured VO2max was obtained from the GXT programs. Differences between the…
Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area
2020
Abstract The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for loa…
Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions
2021
Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Me…
Relative evaluation of regression tools for urban area electrical energy demand forecasting
2019
Abstract Load forecasting is the most fundamental application in Smart-Grid, which provides essential input to Demand Response, Topology Optimization and Abnormally Detection, facilitating the integration of intermittent clean energy sources. In this work, several regression tools are analyzed using larger datasets for urban area electrical load forecasting. The regression tools which are used are Random Forest Regressor, k-Nearest Neighbour Regressor and Linear Regressor. This work explores the use of regression tool for regional electric load forecasting by correlating lower distinctive categorical level (season, day of the week) and weather parameters. The regression analysis has been do…
ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
2020
Author's accepted manuscript Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management s…
A new method for forecasting energy output of a large-scale solar power plant based on long short-term memory networks a case study in Vietnam
2021
Abstract This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. At first, a configuration of the model based on the LSTM algorithm is selected in accordance with the weather and operating conditions of SPP in Vietnam. Not only different structures of LSTM model but also other conventional forecasting methods for time series data are compared in terms of error accuracy of forecast on test data set to evaluate the effectiveness and select the most suitable LSTM configuration. The most suitable config…
Estimating Heart Rate, Energy Expenditure, and Physical Performance With a Wrist Photoplethysmographic Device During Running
2017
BackgroundWearable sensors enable long-term monitoring of health and wellbeing indicators. An objective evaluation of sensors’ accuracy is important, especially for their use in health care. ObjectiveThe aim of this study was to use a wrist-worn optical heart rate (OHR) device to estimate heart rate (HR), energy expenditure (EE), and maximal oxygen intake capacity (VO2Max) during running and to evaluate the accuracy of the estimated parameters (HR, EE, and VO2Max) against golden reference methods. MethodsA total of 24 healthy volunteers, of whom 11 were female, with a mean age of 36.2 years (SD 8.2 years) participated in a submaximal self-paced outdoor running test and maximal voluntary exe…