Location-aware green energy availability forecasting for multiple time frames in smart buildings: The case of Estonia

TL;DR

This paper develops and compares machine learning models to accurately forecast solar PV power output in smart buildings, considering location-specific weather data for Estonia across multiple time frames.

Contribution

It introduces a location-aware forecasting approach using various machine learning models to improve PV power prediction accuracy in smart buildings.

Findings

Random forest achieved the highest accuracy among models.

Weather features significantly influence PV output predictions.

Multi-time frame forecasting enhances energy management strategies.

Abstract

Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean energy for everyone. Among the world's all renewable resources, solar energy is considered as the most abundant and can certainly fulfill the target of SDGs. Solar energy is converted into electrical energy through Photovoltaic (PV) panels with no greenhouse gas emissions. However, power generated by PV panels is highly dependent on solar radiation received at a particular location over a given time period. Therefore, it is challenging to forecast the amount of PV output power. Predicting the output power of PV systems is essential since several public or private institutes generate such green energy, and need to maintain the balance between demand and supply. This research aims to forecast PV system output power based on weather and derived features using different machine learning models. The objective is to obtain the best-fitting model to precisely predict output power by inspecting the data. Moreover, different performance metrics are used to compare and evaluate the accuracy under different machine learning models such as random forest, XGBoost, KNN, etc.