Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A receptor-like mechanosensitive protein governs preprophase band positioning for asymmetric cell divisions and SC morphogenesis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Retraction Note: Forecasting for electricity demand utilizing enhanced inception-V4 using improved Osprey optimization.

Scientific reports·2026
Same author

Recent Advances in Multi-Omics of Systemic Lupus Erythematosus.

Current rheumatology reviews·2026
Same author

Implementing a novel PAM-4 modulation/demodulation scheme along with source and link protection in a high capacity data center architecture.

PloS one·2026
Same author

Parametric assessment of rainfall-related slope stability through SRM modeling and orthogonal experimental design: insights from the Zhuquedong slope, China.

Scientific reports·2026
Same author

Visualizing application of RUBY-MAT system in maize genetic transformation and gene expression pattern detection.

Plant cell reports·2026

Related Experiment Video

Updated: Jul 1, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Modified artificial neural network based on developed snake optimization algorithm for short-term price prediction.

Baozhu Li1, Majid Khayatnezhad2

  • 1College of Computer Science, Huanggang Normal University, Huanggang, 438000, China.

Heliyon
|March 7, 2024
PubMed
Summary

This study introduces a novel Artificial Neural Network (ANN) combined with a Snake Optimization Algorithm (SOA) for accurate short-term electricity price prediction. The new method significantly outperforms existing algorithms, offering improved market decision-making.

Keywords:
Developed snake optimization algorithmNeural networkPrice forecastingShort-term prediction

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 1, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Energy Economics
  • Computational Intelligence
  • Time Series Forecasting

Background:

  • Short-term electricity price prediction is vital for market participants to optimize bidding and manage risk.
  • Electricity prices exhibit high volatility and nonlinearity due to factors like supply, demand, weather, and renewables.
  • Accurate forecasting is challenged by complex market dynamics and external influences.

Purpose of the Study:

  • To develop and evaluate a novel approach for short-term electricity price forecasting.
  • To enhance prediction accuracy by optimizing Artificial Neural Networks (ANN) using a Snake Optimization Algorithm (SOA).
  • To assess the proposed method's performance against established optimization techniques in the Nord Pool market.

Main Methods:

  • A hybrid model combining Artificial Neural Networks (ANN) with a novel Snake Optimization Algorithm (SOA).
  • SOA is used to optimize ANN structure, weights, and select relevant input data based on price curve and wind production similarity.
  • Experimental validation using data from DK-1 and SE-1 regions in the Nord Pool market across various seasons and time horizons.

Main Results:

  • The proposed ANN-SOA technique demonstrated superior accuracy in short-term price prediction compared to Particle Swarm Optimization (PSO) and Genetic Algorithms (PSOGANN) and Gravitational Search Optimization Algorithm (GSONN) based methods.
  • Achieved an average Mean Absolute Percentage Error (MAPE) of 3.1292% in DK-1, outperforming PSOGA by 32.5% and GSONN by 47.1%.
  • Achieved an average MAPE of 2.7621% in SE-1, outperforming PSOGA by 40.4% and GSONN by 64.7%.

Conclusions:

  • The novel ANN-SOA approach offers a significant improvement in short-term electricity price forecasting accuracy.
  • The method effectively handles the volatility and nonlinearity inherent in electricity markets.
  • This technique provides a valuable tool for market participants to enhance strategic decision-making and planning.