Energy and Power En gi neering, 2011, 3, 9-16

doi:10.4236/epe.2011.31002 Published Online February 2011 (http://www.SciRP.org/journal/epe)

Copyright © 2011 SciRes. EPE

Short-Term Electricity Price Forecasting Using a

Combination of Neural Networks and Fuzzy Inference

Evans Nyasha Chogumaira, Takashi Hiyama

Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan

E-mail: evans@st.cs.kumamoto-u.ac.jp

Received October 28, 201; revised November 3, 2010; accepted November 4, 2010

Abstract

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale

electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-

ture of electricity prices on the time domain by clustering the input data into time ranges where the variation

trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-

ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated

electricity demand at the target time is estimated first using a separate ANN. The Australian New-South

Wales electricity market data was used to test the system. The developed system shows considerable im-

provement in performance compared with approaches that regard price data as a single continuous time se-

ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-

riods with price spikes.

Keywords: Electricity Price Forecasting, Short-Term Load Forecasting, Electricity Markets, Artificial Neural

Networks, Fuzzy Logic

1. Introduction

With deregulation in many electricity markets around the

world, knowledge of possible future values of demand

and the corresponding price has become more significant

to the different entities on the market – generators and

electricity traders for determining bidding strategies, and

system operators for administration of the market [1] .

Generally commodity prices are compelled by supply

and demand balance. In electricity markets the traded

‘commodity’ cannot be stockpiled economically, the con-

straints are defined by the syste m total capacity to satisfy

demand at any given time [2]. This therefore causes elec-

tricity prices to have a high probability of volatility,

which masks observable trends necessary for forecasting

future values, especially in the short term.

Short-term forecasts cover the period from a few min-

utes to about one week ahead. These are useful for dis-

patch and short-term or spot trading. Short term trading is

meant to service the short-term variations in load and the

actual prices are only known after matching of bids and

offers by the market operator [1]. This presents a chal-

lenge in that to place effective bids; the traders need to

have an idea of the future values of the demand and its

corresponding price.

Different models have been employed in power sys-

tems for achieving forecasting accuracy and these include:

regression, statistical and state space methods [3-6]. Arti-

ficial intelligence based approaches have been explored

based on expert systems, evolutionary programming,

fuzzy systems, artificial neural networks and various

combinations of these. The widely used approach in pre-

vious works has been based on developing a mathemati-

cal model of the power system and then to perform simu-

lations to determine required values [7-9]. The main

challenge with this has been to make accurate non-linear

mathematical models. Also, complete system data is not

always readily available and the great computational ef-

fort required [2].

To estimate future values of any parameter one needs

to have some information on factors that influence that

parameter, or trends that describe the parameter of inter-

est. A number of parameters have been analyzed to de-

termine their usefulness as inputs data for short term es-

timation. These include weather variables, past demand

and price data. Weather variables such as temperature

used in [10] are either averaged information of forecasted

values, there is always a chance that the uncertainty could