A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Mandal, Paras; Senjyu, Tomonobu; Urasaki, Naomitsu; Funabashi, Toshihisa; Srivastava, Anurag K.

doi:10.1109/tpwrs.2007.907386

Cited by 198 publications

(191 citation statements)

References 26 publications

Supporting

Mentioning

181

Contrasting

Unclassified

Order By: Relevance

“…A general collective agreement has been found between studies stating that price forecasting in energy markets is crucial for market participants in planning their operations, managing risk, and maximizing benefits [10]- [11].…”

Section: Related Workmentioning

confidence: 86%

See 1 more Smart Citation

Energy cost forecasting for event venues

Zagar

Grolinger

Capretz

et al. 2015

2015 IEEE Electrical Power and Energy Conference (EPEC)

View full text Add to dashboard Cite

Abstract-Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning approaches are considered: k-nearest neighbor (KNN) regression, support vector regression (SVR) and neural networks (NN). These approaches are evaluated on a case study involving a large event venue in Southern Ontario. In terms of prediction accuracy, KNN regression achieved the lowest average error. Error rates varied greatly among different event categories.

show abstract

Section: Related Workmentioning

confidence: 86%

“…In the domain of market price prediction, the focus has been on short timeframes, especially day-ahead prediction [13], [10], [11]. Several popular machine learning techniques have been used in electricity price forecasting, but neural networks appear to be the most dominant.…”

Section: Related Workmentioning

confidence: 99%

Energy cost forecasting for event venues

Zagar

Grolinger

Capretz

et al. 2015

2015 IEEE Electrical Power and Energy Conference (EPEC)

View full text Add to dashboard Cite

show abstract

“…Electricity price forecasting methods based on similar day's methodology were presented by Paras Mandal et al, in numerous works, such as [20][21][22]. Similar price days are selected based on a Euclidian norm.…”

Section: Similar Days Methodologymentioning

confidence: 99%

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Matijaš

Delimar

2014

Energies

View full text Add to dashboard Cite

Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

show abstract

“…Because of all these uncertain factors, the price time series in almost all markets exhibits non-stationary means and variances. Several methods that involve both statistical methods like ARMA, ARIMA, and GARCH [3,40,51] models and intelligent system techniques like ANN, FIS, SVM [11,52,53,14,16], etc. have been proposed by researchers for day-ahead electricity price forecasting incorporating different uncertain effects in the deregulated electricity markets.…”

Section: Dataset-1mentioning

confidence: 99%

“…Significant research efforts have been undertaken to mine these highly chaotic time series databases in the way of either forecasting or classifying patterns in the database. Many techniques have been employed over the years for time series forecasting purpose, including statistical methods like Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models [1][2][3][4][5], intelligence techniques like artificial neural networks (ANNs) [6][7][8][9][10][11], fuzzy inference system (FIS) [12][13][14][15], and support vector machines (SVMs) [16][17][18][19][20], etc. Moreover, time series models like ARIMA, and GARCH, have also been proven to be effective in the stock and electricity price forecasting/modeling.…”

Section: Introductionmentioning

confidence: 99%

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

Parida¹,

Bisoi²,

Dash³

et al. 2017

IJCIS

View full text Add to dashboard Cite

The model proposed in this paper, is a hybridization of fuzzy neural network (FNN) and a functional link neural system for time series data prediction. The TSK-type feedforward fuzzy neural network does not take the full advantage of the use of the fuzzy rule base in accurate input-output mapping and hence a hybrid model is developed using the Chebyshev polynomial functions to construct the consequent part of the fuzzy rules. The model to be known as locally recurrent neuro fuzzy information system (LRNFIS) is used to provide an expanded nonlinear transformation to the input space thereby increasing its dimension which will be adequate to capture the nonlinearities and chaotic variations in the time series. The locally recurrent nodes will provide feedback connections between outputs and inputs allowing signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. For training the proposed LRNFIS, an improved firefly-harmony search (IFFHS) learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters. Three real world time series databases like the electricity price of PJM electricity market, the widely studied currency exchange rates between US Dollar (USD) and other four currencies i.e. Australian Dollar (AUD), Swiss Franc (CHF), Mexican Peso (MXN), Brazilian Real (BRL), along with S&P 500 and Nikkei 225 stock market data are used for performance validation of the newly proposed LRNFIS.

show abstract

A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Cited by 198 publications

References 26 publications

Energy cost forecasting for event venues

Energy cost forecasting for event venues

Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Times Series Forecasting using Chebyshev Functions based Locally Recurrent neuro-Fuzzy Information System

Contact Info

Product

Resources

About