Long-term load forecasting in high resolution for all countries globally
Toktarova, Alla (2017)
Diplomityö
Toktarova, Alla
2017
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe201708118084
https://urn.fi/URN:NBN:fi-fe201708118084
Tiivistelmä
Electricity demand modelling is the central and integral issue for the planning and operation of electric utilities, energy suppliers, system operators and other market participants. Load forecasting provides important information for electricity network planning, and it is essential for the electricity system development. The increase of interest in these issues has occurred as a result of liberalization of power markets, the aging infrastructure and high penetration rate of renewables.
In this research, a methodology is proposed for modelling and forecasting electricity demand. The major advantage of the proposed approach is that it enables the possibility of making short-and long-term hourly load forecasting within a single framework for all countries. The method is constructed and verified using 56-real load data of diverse countries. The accuracy of proposed model function is represented in terms of R-squared error.
The model estimates the amplitude of demand fluctuations for certain significant frequencies and generates the total hourly demand curve for a given year, based on a superposition of sine functions. The initial step was to construct the database including socio-economic and meteorological data for all countries. The world socio-economic scenario is projected from historical values using logistic growth functions. Based on this socio-economic scenario, the annual total demand and peak demand were obtained for all countries, for a period from 2017 to 2100. Finally, the sum of various sine functions can be used to calibrate and forecast hourly electricity demand for any country with available input data for any year in the addressed period.
A key result found is that specific economic, technical and climate characteristics, such as high shares of marginal cost generation, air conditioning, impact of tourism and industrial consumption, local temperature and seasonal effects have significant influence on the quality of results. The obtained results could have a significant impact and support in energy transition studies towards sustainability.
In this research, a methodology is proposed for modelling and forecasting electricity demand. The major advantage of the proposed approach is that it enables the possibility of making short-and long-term hourly load forecasting within a single framework for all countries. The method is constructed and verified using 56-real load data of diverse countries. The accuracy of proposed model function is represented in terms of R-squared error.
The model estimates the amplitude of demand fluctuations for certain significant frequencies and generates the total hourly demand curve for a given year, based on a superposition of sine functions. The initial step was to construct the database including socio-economic and meteorological data for all countries. The world socio-economic scenario is projected from historical values using logistic growth functions. Based on this socio-economic scenario, the annual total demand and peak demand were obtained for all countries, for a period from 2017 to 2100. Finally, the sum of various sine functions can be used to calibrate and forecast hourly electricity demand for any country with available input data for any year in the addressed period.
A key result found is that specific economic, technical and climate characteristics, such as high shares of marginal cost generation, air conditioning, impact of tourism and industrial consumption, local temperature and seasonal effects have significant influence on the quality of results. The obtained results could have a significant impact and support in energy transition studies towards sustainability.