Subject: Artificial Intelligence, Machine Learning, Neural Networks, time-series forecasting, electricity price forecasting, day-ahead market forecasting for Croatia, Smart City, Smart energy grids
Year: 2022
Type: Proceedings
Title: Electricity price forecasting for the day-ahead market in Croatia
Author: Kosteski, Kristijan
Author: Dedinec, Aleksandra
Abstract: This paper presents a Machine Learning model, specifically a Neural Network, to forecast the prices of the Croatian day ahead power exchange. The price forecasting is of great importance for building and maintaining the smart energy grids that are a part of Smart Cities around the globe. Knowing the price of electricity can make a switch to renewable energy sources easier to predict and maintain so that it is available to the citizens of the city. The day ahead market requests bids for the next day for all of the 24 hours in that day. From all of the participants’ bids, a price is calculated where supply meets the demand and this is the price that the models try to predict without using the bids from the participants. Predicting the price of electricity for markets that produce a big part of its electricity from renewable sources can be unpredictable and cause a lot of problems for researchers. Croatia is trying to increase production of electricity from renewable sources in the following years, which will make the prices more unpredictable. The data used is collected for the period from 11.02.2016 to 24.08.2021. Multiple Neural Network structures were explored, but the best one was a Neural Network with 2 hidden layers with 11 neurons each. The testing period was the last 20 % of the data as the training and testing data was split 80:20 and some years were dropped for better results. The results for the model were evaluated using multiple evaluation metrics, most of which being error rates.
Publisher:
Relation: The 19th International Conference on Informatics and Information Technologies – CIIT 2022
Identifier: oai:repository.ukim.mk:20.500.12188/25680
Identifier: http://hdl.handle.net/20.500.12188/25680
