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

Li, Baozhu; Khayatnezhad, Majid

doi:10.1016/j.heliyon.2024.e26335

Cited by 2 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Computational intelligence models utilize in load forecasting expert systems, artificial intelligence [41,42] neural networks [43][44][45][46][47], fuzzy logic and wavelets, ICEEMDAN-GS-WT-LSTM-ISSA [15][16][17]. Usually most of computational intelligence models used to calibrate parameters in time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

View full text Add to dashboard Cite

Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

View full text Add to dashboard Cite

show abstract

“…The use of ANN algorithms in economic forecasting deviates from traditional methodologies, providing a datadriven methodology capable of adjusting to the complexities and uncertainties of real-world economic systems [5]. ANNs can use the large amounts of historical data available to make forecasts that account for the dynamic character of economic variables [6].…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

Pingfen Liu

2024

jes

View full text Add to dashboard Cite

This study examines the use of artificial neural network (ANN) algorithms in machine learning for recognizing fluctuation patterns and forecasting economic indicators in the field of political economy. In today's changing global economic scene, economic forecasting is critical for informing policy decisions, directing strategic planning, and managing risks. Traditional statistical approaches and time series analysis have long been used for this purpose, but the advent of ANN algorithms provides a new way to capture the intricate dynamics of economic systems. Using ANNs' intrinsic flexibility and nonlinear modelling capabilities, this study analyzes their ability to effectively forecast future swings in economic indices. This work intends to understand the strengths, limits, and practical consequences of ANN-based forecasting models by conducting a complete assessment of literature, methodological methods, and empirical studies. This study adds to the continuing discussion of economic forecasting approaches by diving into theoretical frameworks, methodological concerns, and empirical data. Finally, incorporating ANN algorithms into machine learning offers promising opportunities for improving the understanding of economic processes and supporting informed decision-making in the political economy.

show abstract

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

Cited by 2 publications

References 46 publications

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

Contact Info

Product

Resources

About