Short-Term Load Forecasting Using Generalized Regression and Probabilistic Neural Networks in the Electricity Market

Tripathi, M. M.; Upadhyay, K. G.; Singh, Sadhna

doi:10.1016/j.tej.2008.09.016

Cited by 47 publications

(21 citation statements)

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“…Tripathi et al [80] developed a generalized regression and probabilistic neural networks based short term load forecast model in order to predict the load demand of Australia's Victoria grid. Therefore, in order to improve the forecast accuracy, electricity prices are included along with previous load and respective weather data as forecast model inputs.…”

Section: Ann With Expert System and Regression Techniquementioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

799

286

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Section: Ann With Expert System and Regression Techniquementioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

799

286

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“…Various techniques have been developed for electricity demand forecasting during the past few years. Several research works have been carried out on the application of artificial intelligence (AI) techniques such as fuzzy inference, fuzzy-neural models, artificial neural network (ANN) to the load forecasting problem as AI tools have performed better than conventional methods in shortterm load forecasting [5]- [12]. This paper discusses significant role of ANN in dayahead load forecasting of UPPCL, that is, the hourly forecast of the power system load over a day.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting of UPPCL using ANN

Pandey

Sahay

Tripathi

et al. 2014

2014 6th IEEE Power India International Conference (PIICON)

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Power sector reforms have been introduced in UttarPradesh, India in as early as 1999. Restructuring and unbundling of UP state electricity board was done by segregating power generation, transmission and distribution functions into autonomous and separately accountable entities. Uttar Pradesh power corporation India ltd. (UPPCL) was formed. An independent Regulatory Body was formed. Private sector participation was encouraged and tariff reform was introduced with the objective to rationalize tariff for full cost recovery and minimize cross subsidy. Power is procured through long-term PPA or energy exchange based on forecasting of day-ahead load demand. The present load forecasting method is based on previous year load and uses very crude method with large errors. This paper discusses role of ANN in day-ahead hourly forecast of the power system load in UPPCL so as to minimize the error in demand forecasting. A new artificial neural network (ANN) has been designed to compute the forecasted load of UPPCL. The data used in the modeling of ANN are hourly historical data electricity load. The ANN model is trained on hourly data from UPPCL from April, 2014 to June, 2014 and tested on out-ofsample data of two weeks. Simulation results obtained have shown that day-ahead hourly forecasts of load using proposed ANN is very accurate with very less error.

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“…Among the different techniques of forecasting, application of ANN for forecasting in power system has received much attention in recent years [6]- [9]. The main reason of ANN becoming so popular lies in its ability to learn complex and nonlinear relationships that are difficult to model with conventional techniques [10].…”

Section: Introductionmentioning

confidence: 99%

Day ahead hourly load and price forecast in ISO New England market using ANN

Sahay

Tripathi

2013

2013 Annual IEEE India Conference (INDICON)

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In restructured daily power markets, forecasting electricity price and load are most essential tasks and basis for any decision making. Short-term load forecasting is an essential instrument in power system planning, operation, and control. Also, the accurate day ahead electricity price forecasting provides crucial information for power producers and consumers to develop accurate bidding strategies in order to maximize their profit. In this paper artificial intelligence (AI) has been applied in short-term load and price forecasting that is, the day-ahead hourly forecast of the electricity market parameters (load and price) over a week. Neural network fitting tool of MATLAB Software has been used to compute the forecasted load and price in ISO New England market. The data used in the forecasting are hourly historical data of the temperature, electricity load and natural gas price of ISO New England market. The ANN was trained on hourly data from the 2007 to 2011 and tested on outof-sample data from 2012. The simulation results have shown highly accurate day-ahead forecasts with very small error in load and price forecasting.Keywords-Day ahead electricity price forecast, locational marginal price (LMP), mean absolute percentage error, neural network, power system, short-term load forecasting.

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Short-Term Load Forecasting Using Generalized Regression and Probabilistic Neural Networks in the Electricity Market

Cited by 47 publications

References 0 publications

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Short-term load forecasting of UPPCL using ANN

Day ahead hourly load and price forecast in ISO New England market using ANN

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