Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market

Meisam Sadi; Ahamad Arabkoohsar

doi:10.1109/ICSPIS65223.2024.10931094

Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market

Meisam Sadi^*
, Ahamad Arabkoohsar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this paper, the impact of electricity price variation on operational expenses is studied for the heat production unit, for which an AI-based algorithm is used to calculate the unit's performance. The unit is connected to the storage tank to assist the production unit in overproducing heat when the electricity price is high. In this study, an ANN-based method is intended to adopt a heat production unit to model the unit's performance efficiently. Given this situation, this method is well-suited for modeling tasks and does not need system-specific knowledge. A value of 0.066 for RMS and 0.999 for R² value are achieved with the suggested adjustment of determining parameters. The improvement performance mechanism could improve the system's performance by reducing the system's operational cost by up to 37% when fluctuation occurs at low energy price conditions.

Original language	English
Title of host publication	10th International Conference on Signal Processing and Intelligent Systems (ICSPIS)
Publisher	IEEE
Publication date	2024
Pages	244-247
ISBN (Electronic)	979-8-3315-3254-3
DOIs	https://doi.org/10.1109/ICSPIS65223.2024.10931094
Publication status	Published - 2024
Event	10th International Conference on Signal Processing & Intelligent Systems - Shahrood University of Technology, Shahrood , Iran, Islamic Republic of Duration: 25 Dec 2024 → 26 Dec 2024

Conference

Conference	10th International Conference on Signal Processing & Intelligent Systems
Location	Shahrood University of Technology
Country/Territory	Iran, Islamic Republic of
City	Shahrood
Period	25/12/2024 → 26/12/2024

Keywords

ANN method
Feed-forward neural network
Heat production unit
Day-ahead market
Electricity price

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10.1109/ICSPIS65223.2024.10931094

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@inproceedings{b79aa143f1e44ebfa5967ea2ccf271a7,

title = "Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market",

abstract = "In this paper, the impact of electricity price variation on operational expenses is studied for the heat production unit, for which an AI-based algorithm is used to calculate the unit's performance. The unit is connected to the storage tank to assist the production unit in overproducing heat when the electricity price is high. In this study, an ANN-based method is intended to adopt a heat production unit to model the unit's performance efficiently. Given this situation, this method is well-suited for modeling tasks and does not need system-specific knowledge. A value of 0.066 for RMS and 0.999 for R2 value are achieved with the suggested adjustment of determining parameters. The improvement performance mechanism could improve the system's performance by reducing the system's operational cost by up to 37\% when fluctuation occurs at low energy price conditions.",

keywords = "ANN method, Feed-forward neural network, Heat production unit, Day-ahead market, Electricity price",

author = "Meisam Sadi and Ahamad Arabkoohsar",

year = "2024",

doi = "10.1109/ICSPIS65223.2024.10931094",

language = "English",

pages = "244--247",

booktitle = "10th International Conference on Signal Processing and Intelligent Systems (ICSPIS)",

publisher = "IEEE",

address = "United States",

note = "10th International Conference on Signal Processing \& Intelligent Systems ; Conference date: 25-12-2024 Through 26-12-2024",

}

Sadi, M & Arabkoohsar, A 2024, Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market. in 10th International Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, pp. 244-247, 10th International Conference on Signal Processing & Intelligent Systems, Shahrood , Iran, Islamic Republic of, 25/12/2024. https://doi.org/10.1109/ICSPIS65223.2024.10931094

Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market. / Sadi, Meisam ; Arabkoohsar, Ahamad.
10th International Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2024. p. 244-247.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market

AU - Sadi, Meisam

AU - Arabkoohsar, Ahamad

PY - 2024

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N2 - In this paper, the impact of electricity price variation on operational expenses is studied for the heat production unit, for which an AI-based algorithm is used to calculate the unit's performance. The unit is connected to the storage tank to assist the production unit in overproducing heat when the electricity price is high. In this study, an ANN-based method is intended to adopt a heat production unit to model the unit's performance efficiently. Given this situation, this method is well-suited for modeling tasks and does not need system-specific knowledge. A value of 0.066 for RMS and 0.999 for R2 value are achieved with the suggested adjustment of determining parameters. The improvement performance mechanism could improve the system's performance by reducing the system's operational cost by up to 37% when fluctuation occurs at low energy price conditions.

AB - In this paper, the impact of electricity price variation on operational expenses is studied for the heat production unit, for which an AI-based algorithm is used to calculate the unit's performance. The unit is connected to the storage tank to assist the production unit in overproducing heat when the electricity price is high. In this study, an ANN-based method is intended to adopt a heat production unit to model the unit's performance efficiently. Given this situation, this method is well-suited for modeling tasks and does not need system-specific knowledge. A value of 0.066 for RMS and 0.999 for R2 value are achieved with the suggested adjustment of determining parameters. The improvement performance mechanism could improve the system's performance by reducing the system's operational cost by up to 37% when fluctuation occurs at low energy price conditions.

KW - ANN method

KW - Feed-forward neural network

KW - Heat production unit

KW - Day-ahead market

KW - Electricity price

U2 - 10.1109/ICSPIS65223.2024.10931094

DO - 10.1109/ICSPIS65223.2024.10931094

M3 - Article in proceedings

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EP - 247

BT - 10th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

PB - IEEE

T2 - 10th International Conference on Signal Processing & Intelligent Systems

Y2 - 25 December 2024 through 26 December 2024

ER -

Enhancing Heat Production by AI-Driven Performance Analysis, Impact of Electricity Price Variations in Day-Ahead Market

Abstract

Conference

Keywords

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