Electricity price forecasting – ARIMA model approach
Tina Jakaša #1, Ivan Andročec #2, Petar Sprčić *3
Hrvatska elektroprivreda Ulica grada Vukovara 37, Zagreb, Croatia 2
* HEP Trade Ulica grada Vukovara 37, Zagreb, Croatia
Abstract— Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze and forecast time series. The model is applied to time series consisting of day-ahead electricity prices from EPEX power exchange.
II. CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING CRISP-DM is a commonly used standard that describes a life cycle of a data mining process 3 . The life cycle consists of six phases, as shown in Fig.1.
I. INTRODUCTION Electricity is among the most volatile of commodities. Daily average change of the spot electricity price can be up to 50 %, while at the same time for other commodities is up to 5 %. There are many market players depending on electricity price trends, such as generators, traders, suppliers and end customers (particularly large industrial customers). Clearly it is very important for them to have accurate forecasting models for electricity prices. The paper focuses on forecasting day-ahead electricity prices using European Energy Exchange data as the reference power market. EEX cooperates with the French Powernext SA. EEX holds 50% of the shares in the joint venture EPEX Spot SE based in Paris which operates short-term trading in power – the so-called Spot Market – for Germany, France, Austria and Switzerland 1 . An electricity spot market represents a day-ahead market. A spot contract is normally an ordinary hourly contract for the physical delivery of energy. The mechanism of determination is a closed auction that is conducted once a day 2 . The hypotheses test that ARIMA models is good enough to forecast day-ahead electricity prices. ARIMA models have been already applied for price forecasting but usually simple, on smaller number of observation, usually three weeks data up to one year. In this paper, the original dataset has 3836 observations (10 years). The expert modeler is used to find the best fitted ARIMA model.
Fig. 1. Phases of the CRISP-DM reference model The process starts with first step Business understanding, converting the business knowledge into data mining problem definition. Step two is Data understanding, analysing data sets, discovering first insights into the data to form hypotheses. During the third step, Data preparation, we prepare a final dataset as well as perform necessary data cleaning and transformation. Step four is Modelling, when we apply different modelling technique to resolve the data mining problem. The fifth step is called Evaluation, a very important step. In this phase we examine a goodness of the model and if needed, we can still improve the model before using it. Last step is Deployment, applying the model on real data. After each step we can decide to go forward or backward, depending on the result. This process is based on iterations. A. Business and data understanding The problem of this research is to model electricity dayahead prices to be able to use it for forecasting. The originally hourly day-ahead prices (24 hours) for German electricity market (www.epexspot.com) for the period 2000-2011 is used. A time series of daily arithmetic means is drawn from trading
978-1-61284-286-8/11/$26.00 ©2011 IEEE
2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb, Croatia...