The Investigation Focuses on the Development of a Forecasting Model for Electricity Demand, Utilizing a Fuzzy Time Series Approach DOI
Li Liu, Wei Zhang, Chao Ji

et al.

Published: Nov. 3, 2023

Electricity demand forecasting is of great significance in the field energy, which helps rational planning and management electricity resources. The aim this study to develop an model, based on a fuzzy time series analysis approach. A large-scale dataset containing time, actual values, forecast data provided by Transmission System Operator (TSO) used. covers development evaluation univariate multivariate models. For models, we implemented HOFTS, WHOFTS PWFTS results show that model performs well all orders clearly outperforms predictive performance TSO. achieved impressive accuracy MAPE values as low 0.87%. In terms MVFTS, Weighted FIG-FTS models were applied, making full use partitioning weight assignment. Although these failed outperform TSO performance, they demonstrated lower errors forecasting, showing advantages dealing with complex correlated data.

Language: Английский

Forex Market Analysis Using Deep Learning Approaches DOI

Kalluri Ram Rohith Reddy,

Kankanala Kowsick Raja,

P. Subham

et al.

Journal of Advanced Computer Science & Technology, Journal Year: 2024, Volume and Issue: 12(2), P. 41 - 52

Published: July 4, 2024

This paper compares the effectiveness of various deep learning models which includes LSTM (Long-Short Term Memory) and GRU (Gated Recurrent Unit) models. These use three exchange currency pairs named Euro to US Dollar, British Pound Indian Rupee Japanese Yen for purpose training performance comparison. The analysis is conducted daily according time zones. Mean Square Error (MSE), Root (RMSE), Absolute (MAE) measures are used compare different According observations, model outperformed in majority datasets.

Language: Английский

Citations

0
Developing a RBFN-Based Enhanced Web Crawler for Tamil Text Categorization DOI

P M Suresh,

K. Raja

Published: May 24, 2024

Language: Английский

Citations

0
Predicting Stock Market Prices Using a Hybrid of High-Order Neural Networks and Barnacle Mating Optimization DOI
Sudersan Behera, A. V. S. Pavan Kumar, Sarat Chandra Nayak

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 279 - 290

Published: Jan. 1, 2024

Language: Английский

Citations

0
Financial Time Series Forecasting Using Hybrid Evolutionary Extreme Learning Machine DOI
Sudersan Behera,

G. Kadirvelu,

P. Sambasiva Rao

et al.

Algorithms for intelligent systems, Journal Year: 2024, Volume and Issue: unknown, P. 93 - 103

Published: Jan. 1, 2024

Language: Английский

Citations

0
High-precision and low-overhead defect object detection model based on LP-DETR+SAA+: Focusing on road defects in high-noise environments DOI
Huaizheng Lu,

Dedong Zhang,

Ruoxue Li

et al.

Published: Aug. 7, 2024

Language: Английский

Citations

0
Emergence of AI—Impact on Building Condition Index (BCI) DOI Creative Commons
J. D. West, Milind Siddhpura, Ana Catarina Jorge Evangelista

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3868 - 3868

Published: Dec. 2, 2024

The Building Condition Index (BCI) is a widely adopted quantitative metric for assessing various aspects of building’s condition, as it facilitates decision-making regarding maintenance, capital improvements and, most importantly, the identification investment risk. In practice, longitudinal BCI scores are typically used to identify maintenance liabilities and trends proactively provide indications when strategies need be altered. This allows more efficient resource allocation helps maximise lifespan functionality buildings their assets. Given historical ambiguity concerns because reliance on visual inspections, this research investigates how AI using ANN, DNN CNN can improve predictive accuracy determining recognisable Index. It demonstrates ANN perform over asset classes (apartment complexes, education commercial buildings). results suggest that architecture adept at dealing with diverse complex datasets, thus enabling versatile prediction model building categories. envisaged expansion maturity CNN, calculation methodologies will become sophisticated, automated integrated traditional assessment approaches.

Language: Английский

Citations

0
Optimization of Deep Learning Models for Non-stationary Time Series Data DOI
Miao Chen, Zhenghui Zhao

Published: Aug. 23, 2024

Language: Английский

Citations

0
The Investigation Focuses on the Development of a Forecasting Model for Electricity Demand, Utilizing a Fuzzy Time Series Approach DOI
Li Liu, Wei Zhang, Chao Ji

et al.

Published: Nov. 3, 2023

Electricity demand forecasting is of great significance in the field energy, which helps rational planning and management electricity resources. The aim this study to develop an model, based on a fuzzy time series analysis approach. A large-scale dataset containing time, actual values, forecast data provided by Transmission System Operator (TSO) used. covers development evaluation univariate multivariate models. For models, we implemented HOFTS, WHOFTS PWFTS results show that model performs well all orders clearly outperforms predictive performance TSO. achieved impressive accuracy MAPE values as low 0.87%. In terms MVFTS, Weighted FIG-FTS models were applied, making full use partitioning weight assignment. Although these failed outperform TSO performance, they demonstrated lower errors forecasting, showing advantages dealing with complex correlated data.

Language: Английский

Citations

0