This project forecasts 24-hour electricity consumption using historical consumption and weather data from various regions in Norway. It implements and compares different neural network architectures, including LSTMs, CNNs, and feed-forward networks, to evaluate their performance in time-series forecasting.

• Goal: Predict power consumption 24 hours ahead using neural networks.
• Data: Historical hourly consumption and temperature data from 2017-2023 for 5 bidding areas in Norway.
• Models: LSTMs, CNNs, and feed-forward networks trained on historical data.