Gradient Boosting Weather Forecasting in Finland : The Impact of Climate Change on Renewable Energy Production

Abstract

Climate change is a serious global phenomenon caused by human activities such as burning fossil fuels and deforestation, resulting in rising temperatures and more persistent weather events. Climate change has impacted Vaasa with an increase in sea levels, leading to a higher risk of flooding of coastal areas of Finland, from rising temperatures to more extreme weather events such as storms and heavy rainfall, causing damage to infrastructure and disrupting transportation and communication networks. Transition to a more sustainable future is inevitable to tackle one of the planet’s biggest social challenges. This thesis aims to integrate an in-depth historical data analysis from the Vaasa area to forecast weather-dependent renewable energy generation by modeling an Artificial Intelligence (AI) machine learning technique called gradient boosting, focusing on wind, hydro, and solar generation.

Machine learning algorithms help to identify patterns and trends to forecast future energy production with the help of weather condition data. Using a gradient boosting model, a long-term time series analysis technique of climate variability has been designed to produce accurate forecasts of energy generation levels in Finland. By dividing the historical data into training and testing sets, the model can evaluate its performance and adjust its parameters. The model is taught to recognize patterns and trends. Adjustments are made to improve accuracy. It is then used to predict different future weather conditions and energy consumption levels with momentary observations. The area of research is the city of Vaasa, where En.ilmatieteenlaitos.fi historical data from 2015 to 2024 is collected to be analyzed. Forecasted data includes momentary observations of air temperature and pressure, humidity, wind speed and direction, and precipitation. The research focuses on predicting weather patterns 10 years ahead. Analysis projects evaluation of the sustainability of current renewable energy sources in Finland as well as the flexibility needs of the Transmission system operator (TSO) and Distribution system operator (DSO) more accurately in different parts of the transmission and distribution networks, related to possible voltage or thermal limit violations, frequency control needs, to improve real-time and short-term operation and operation planning of the power system. This information can help energy system operators make better decisions to guarantee sustainability, efficiency, and lower overall costs by combining AI to predict weather conditions and their impact on energy production.

The findings suggest that Finland's potential for energy independence and economic prosperity relies on the greenhouse gas effect and is possible with government initiatives to take advantage of the coming climate change in the country, mainly affecting wind, solar, and hydropower. Advanced energy storage may be the key solution when positive weather trends align with Finland's renewable energy goals. After fulfilling the maximum weather renewable output that Finland benefits from, integrated renewable systems and adaptive strategies will reduce fossil fuel dependency achieving energy independence and combating climate change impacts.