A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting

Mandal, Paras; Haque, Ashraf Ul; Meng, Julian; Srivastava, Anurag K.; Martinez, Ralph

doi:10.1109/tpwrs.2012.2222452

Cited by 92 publications

(43 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Out-sample data set: all the hours of the weeks with numbers 5,10,15,20,25,30,35,40,45,50 in 2012, and weeks number 2, 7, 12, 17, 22, 27, 32, 37, 42, 47 in 2013; a total of 3360 cases (h).…”

Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

View full text Add to dashboard Cite

This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

show abstract

Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The Fuzzy ARTMAP is a new concept for electricity price forecasting [72], it has already been applied for wind speed forecasting [73] and load forecasting [74]. Mostly conventional neural networks suffers from plasticity-stability dilemma, i.e.…”

Section: Fuzzy Artmap Based Modelmentioning

confidence: 99%

“…Fuzzy ARTMAP also actualize a new min-max learning rule that collectively Figure 8. Architecture of fuzzy ARTMAP [72]. minimizes predictive error and maximizes generalization, or code compression.…”

Section: Fuzzy Artmap Based Modelmentioning

confidence: 99%

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Singh¹,

Mohanty²

2015

JPEE

View full text Add to dashboard Cite

In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models; and 2) Simulation based models; time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

show abstract

“…In [30], authors have used a combination of novel hybrid intelligent techniques consisting of the wavelet transform, firefly algorithm and soft computing model based on the fuzzy network to forecast day-ahead electricity price in the Ontario market and Pennsylvania-New Jersey-Massachusetts (PJM) market data. This model showed 40% improvement in forecast error than other hybrid models.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

et al. 2018

View full text Add to dashboard Cite

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

show abstract

A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting

Cited by 92 publications

References 32 publications

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Contact Info

Product

Resources

About