Comparison of Neural Network-Based Forecasting Methods Using Multi-Criteria Decision-Making Tools

Borade, Atul B.; Bansod, Satish V.

doi:10.1080/16258312.2011.11517276

Cited by 6 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three models were chosen as the best for predicting future energy use, and these three models were combined in an ANN to do so. They created the ANN using back-propagation training with a feed-forward multilayer perceptron neural network [27,32,35,42]. An error-based comparison was performed between the proposed model and the classical back propagation-trained ANN model [43].…”

Section: Simulation Resultsmentioning

confidence: 99%

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

2022

IJE

View full text Add to dashboard Cite

Since load time series are very changeable, demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural networks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

2022

IJE

View full text Add to dashboard Cite

show abstract

“…Ref. [27] compared the sales forecasts of five neural networks in terms of supply chain-related costs. They derived the costs by looking at the deviations of the forecast and the actual demand for every period individually, not considering the development of inventory levels.…”

Section: Cost-considering Sales Forecast Evaluationmentioning

confidence: 99%

A Dynamic Systems Model for an Economic Evaluation of Sales Forecasting Methods

Kuhlmann

Pauly

2023

Teh. glas. (Online)

View full text Add to dashboard Cite

Sales forecasts are essential for a smooth workflow and cost optimization. Usually, they are assessed using statistical error measures, which might be misleading in a business context. This paper proposes a new dynamic systems model for an economic evaluation of sales forecasts. The model describes the development of the inventory level over time and derives the resulting overstock and shortage costs. It is tested on roughly 3,000 real-world time series and compared with the commonly used approach based on statistical measures. The experiments show that different statistical measures have no coherent evaluation, making their usage even less suitable for a practical economic application.

show abstract