Jove
Visualize
Contact Us

Related Concept Videos

Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
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...
105
Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Manipulation and Analysis01:21

Manipulation and Analysis

19
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
19
Econometric Views (EViews)01:29

Econometric Views (EViews)

117
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
117
Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

27
Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
27
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

173
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
173

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Architecture for Enhancing Communication Security with RBAC IoT Protocol-Based Microgrids.

Sensors (Basel, Switzerland)·2024
Same author

Performance Analysis of Routable GOOSE Security Algorithm for Substation Communication through Public Internet Network.

Sensors (Basel, Switzerland)·2023
Same author

An Interoperable Communication Framework for Grid Frequency Regulation Support from Microgrids.

Sensors (Basel, Switzerland)·2021
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 6, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

251

Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand

Myung-Joo Park1, Hyo-Sik Yang1

  • 1Department of Computer Science and Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

This study compares ARIMA, SARIMA, LSTM, and SVM for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. SVM and SARIMA show strengths in handling volatility and seasonality, respectively, guiding optimal model selection for energy management.

Keywords:
AMIARIMALSTMSARIMASVMdemand responseshort-term forecastingsmart grid

More Related Videos

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

1.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Related Experiment Videos

Last Updated: Jun 6, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

251
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

1.7K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Area of Science:

  • Energy Systems Engineering
  • Data Science
  • Electrical Engineering

Background:

  • Accurate short-term load forecasting is crucial for effective demand response (DR) strategies in smart grids.
  • Advanced Metering Infrastructure (AMI) provides real-time data essential for improving forecasting models.
  • Evaluating diverse time series algorithms is necessary to optimize energy management and grid stability.

Purpose of the Study:

  • To compare the performance of four time series forecasting algorithms: ARIMA, SARIMA, LSTM, and SVM.
  • To assess their applicability and effectiveness for short-term load forecasting using AMI data.
  • To provide guidelines for selecting the best forecasting model based on data characteristics and application requirements.

Main Methods:

  • Comparative analysis of ARIMA, SARIMA, LSTM, and SVM algorithms.
  • Evaluation based on predictive accuracy, computational efficiency, and scalability.
  • Utilizing a dataset of real-time electricity consumption from AMI systems.

Main Results:

  • Support Vector Machines (SVM) excelled in predicting nonlinear patterns and high volatility.
  • Seasonal AutoRegressive Integrated Moving Average (SARIMA) effectively captured seasonal electricity consumption trends.
  • Long Short-Term Memory (LSTM) demonstrated potential for complex temporal dependencies but required significant data and tuning.

Conclusions:

  • The choice of forecasting model significantly impacts the efficiency of demand response strategies.
  • Each algorithm possesses unique strengths and weaknesses, necessitating careful selection based on specific data and application needs.
  • Integrating advanced forecasting techniques into smart grids enhances reliability and supports dynamic energy management.