Analysing the economic benefit of electricity price forecast in industrial load scheduling

T Mathaba, Xiaohua Xia, Jiangfeng Zhang

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

The current trend of electricity market deregulation ushers in increasingly dynamic electricity pricing schemes. The cost-optimal scheduling of industrial loads with accurate price forecasts is therefore important. However, results in the current literature suggest that mean absolute percentage error (MAPE) is poor at indicating the economic benefit of a forecast. This paper presents the economic benefit analysis of electricity price forecast on the day-ahead scheduling of load-shifting industrial plants. A coal-conveying system with storage is used as a case study. The research uses three price forecasting methods on the PJM's market prices over a period of two years. Rank correlation (RC) between the predicted price and the actual price is proposed as an indicator of economic benefit. The results show that RC is a better indicator of economic benefit than root mean square error (RMSE) and MAPE. They also show that potential economic benefit obtainable from forecasts depends on price volatility and not mean price. An artificial forecast is used to validate the superiority of RC over MAPE and RMSE. It is observed that the predictability of a forecast's economic benefit is largely dependent on how responsive the load is to electricity price changes.
Original languageEnglish
Pages (from-to)158-165
Number of pages8
JournalElectric Power Systems Research
Volume116
Early online date26 Jun 2014
DOIs
Publication statusPublished - Nov 2014

Fingerprint

Electricity
Scheduling
Economics
Mean square error
Deregulation
Conveying
Industrial plants
Costs
Coal

Keywords

  • price forecast
  • electricity cost optimization
  • rank correlation
  • day-ahead scheduling
  • demand-side management
  • conveyor belt system

Cite this

@article{6ae53da432e848ac972f6132ab8eccb4,
title = "Analysing the economic benefit of electricity price forecast in industrial load scheduling",
abstract = "The current trend of electricity market deregulation ushers in increasingly dynamic electricity pricing schemes. The cost-optimal scheduling of industrial loads with accurate price forecasts is therefore important. However, results in the current literature suggest that mean absolute percentage error (MAPE) is poor at indicating the economic benefit of a forecast. This paper presents the economic benefit analysis of electricity price forecast on the day-ahead scheduling of load-shifting industrial plants. A coal-conveying system with storage is used as a case study. The research uses three price forecasting methods on the PJM's market prices over a period of two years. Rank correlation (RC) between the predicted price and the actual price is proposed as an indicator of economic benefit. The results show that RC is a better indicator of economic benefit than root mean square error (RMSE) and MAPE. They also show that potential economic benefit obtainable from forecasts depends on price volatility and not mean price. An artificial forecast is used to validate the superiority of RC over MAPE and RMSE. It is observed that the predictability of a forecast's economic benefit is largely dependent on how responsive the load is to electricity price changes.",
keywords = "price forecast, electricity cost optimization, rank correlation, day-ahead scheduling, demand-side management, conveyor belt system",
author = "T Mathaba and Xiaohua Xia and Jiangfeng Zhang",
year = "2014",
month = "11",
doi = "10.1016/j.epsr.2014.05.008",
language = "English",
volume = "116",
pages = "158--165",
journal = "Electric Power Systems Research",
issn = "0378-7796",

}

Analysing the economic benefit of electricity price forecast in industrial load scheduling. / Mathaba, T; Xia, Xiaohua; Zhang, Jiangfeng.

In: Electric Power Systems Research, Vol. 116, 11.2014, p. 158-165.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Analysing the economic benefit of electricity price forecast in industrial load scheduling

AU - Mathaba, T

AU - Xia, Xiaohua

AU - Zhang, Jiangfeng

PY - 2014/11

Y1 - 2014/11

N2 - The current trend of electricity market deregulation ushers in increasingly dynamic electricity pricing schemes. The cost-optimal scheduling of industrial loads with accurate price forecasts is therefore important. However, results in the current literature suggest that mean absolute percentage error (MAPE) is poor at indicating the economic benefit of a forecast. This paper presents the economic benefit analysis of electricity price forecast on the day-ahead scheduling of load-shifting industrial plants. A coal-conveying system with storage is used as a case study. The research uses three price forecasting methods on the PJM's market prices over a period of two years. Rank correlation (RC) between the predicted price and the actual price is proposed as an indicator of economic benefit. The results show that RC is a better indicator of economic benefit than root mean square error (RMSE) and MAPE. They also show that potential economic benefit obtainable from forecasts depends on price volatility and not mean price. An artificial forecast is used to validate the superiority of RC over MAPE and RMSE. It is observed that the predictability of a forecast's economic benefit is largely dependent on how responsive the load is to electricity price changes.

AB - The current trend of electricity market deregulation ushers in increasingly dynamic electricity pricing schemes. The cost-optimal scheduling of industrial loads with accurate price forecasts is therefore important. However, results in the current literature suggest that mean absolute percentage error (MAPE) is poor at indicating the economic benefit of a forecast. This paper presents the economic benefit analysis of electricity price forecast on the day-ahead scheduling of load-shifting industrial plants. A coal-conveying system with storage is used as a case study. The research uses three price forecasting methods on the PJM's market prices over a period of two years. Rank correlation (RC) between the predicted price and the actual price is proposed as an indicator of economic benefit. The results show that RC is a better indicator of economic benefit than root mean square error (RMSE) and MAPE. They also show that potential economic benefit obtainable from forecasts depends on price volatility and not mean price. An artificial forecast is used to validate the superiority of RC over MAPE and RMSE. It is observed that the predictability of a forecast's economic benefit is largely dependent on how responsive the load is to electricity price changes.

KW - price forecast

KW - electricity cost optimization

KW - rank correlation

KW - day-ahead scheduling

KW - demand-side management

KW - conveyor belt system

U2 - 10.1016/j.epsr.2014.05.008

DO - 10.1016/j.epsr.2014.05.008

M3 - Article

VL - 116

SP - 158

EP - 165

JO - Electric Power Systems Research

JF - Electric Power Systems Research

SN - 0378-7796

ER -