Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

32
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
32
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Electrical Energy01:10

Electrical Energy

1.2K
Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
1.2K
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

109
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
109

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Machine learning assisted multi-criteria decision-making approaches for site selection: A systematic review.

MethodsX·2026
Same author

Tomato leaf disease and severity prediction using multi-task learning.

BMC plant biology·2026
Same author

An ensemble of deep learning models with falcon optimization assisted diabetic retinopathy diagnosis on retinal fundus images.

Scientific reports·2026
Same author

HierarchicalNets for multi level hierarchical classification of yoga poses.

Scientific reports·2026
Same author

Large language model empowered explainable and interpretable mental health analysis.

Scientific reports·2026
Same author

Rose leaf disease classification and severity estimation using an interpretable vision transformer-based multi-task framework.

BMC plant biology·2026

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.4K

使用数据库组合,组合和混合建模技术进行家庭电力消耗预测.

Gaikwad Sachin Ramnath1, R Harikrishnan2, S M Muyeen3

  • 1Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed) University, Pune, India.

Scientific reports
|October 2, 2024
PubMed
概括

改善家庭电力消耗 (HEC) 预测至关重要. 一种新的混合模型显著提高了准确性,使研究人员和公用事业公司受益.

关键词:
数据质量评估数据质量评估异质整体是一个异质的整体.家庭用电消费量 家庭用电消费量混合动力模型 混合动力模型每个月的预测预测

更多相关视频

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

5.8K

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Published on: January 16, 2020

5.8K

科学领域:

  • 能源科学 能源科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 由于时间变化和影响因素,家庭电力消耗 (HEC) 预测是复杂的.
  • 准确的HEC预测对于能源管理,电网稳定性和政策制定至关重要.
  • 现有的模型在实现高预测准确性方面面临挑战.

研究的目的:

  • 开发和验证一种新的方法来提高HEC预测的准确性.
  • 探索结合不同数据源和应用先进的机器学习技术的影响.
  • 为了解和管理家庭能源使用提供更可靠的工具.

主要方法:

  • 利用了两个主要数据集:来自225名印度消费者的问卷调查 (QS) 和月度消费 (MC).
  • 创建组合数据集 (QS+MC,QsEq+下个月,QsEq+MC) 进行全面分析.
  • 应用了相关性方法,特征工程,数据质量评估,异质集团预测 (HEP) 和混合模型.

主要成果:

  • 随机森林在MC数据集上的RMSE为36.18千瓦时,MAE为25.73千瓦时,R2为0.76.
  • 在QsEq+MC数据集上的自适应提升显示RMSE为36.77千瓦时,MAE为26.18千瓦时,R2为0.76.
  • 拟议的混合型号取得了卓越的结果:RMSE为22.02千瓦时,MAE为13.04千瓦时,R2为0.92.

结论:

  • 混合模型在HEC预测中明显优于单个机器学习算法和HEP模型.
  • 结合不同的数据集和采用先进的技术,如HEP和混合建模是有效提高准确性.
  • 这些发现为研究人员,政策制定者和公用事业公司提供了有价值的见解,以改善能源管理.