Energy Consumption Prediction for Smart Homes

Abstract - Accurate electricity consumption forecasting is a critical component in ensuring the stability, efficiency, and cost- effectiveness of national power systems. In Finland, where the electricity sector is shaped by nuclear energy, cogeneration, renewable sources like black liquor and wood, and significant electricity imports, reliable forecasting becomes even more essential. This research investigates the application of Long Short- Term Memory (LSTM), a type of recurrent neural network (RNN) designed for sequential data, in predicting future energy consumption trends. A univariate time series dataset from Fin- grid, Finland’s transmission system operator, encompassing six years of hourly electricity consumption data, serves as the basis for model training and evaluation. The study aims to assess the model’s performance in capturing temporal patterns and generating accurate short-term forecasts. Our results highlight the capability of LSTM models to effectively model complex energy usage patterns and provide meaningful insights for energy planning and decision-making processes. Key Words: Long short-term memory (LSTM), Time series analysis, Machine learning, Energy prediction, Fingrid, Univariate data, Finland electricity sector.