“Solar Score” — With precise predictions we help you to get the most out of your solar plant.

  1. We had to find suitable weather and solar plant data that we could use for our prediction model.
  2. We had to create a prediction model and train it.
  3. We had to build a website where the user could register and access the prediction.

Machine learning model:

Time series prediction is an old topic in economics and statistics. During the recent acceleration in artificial intelligence, especially in deep neural networks, forecasting of sequences was made possible with AI as well.

  • Predict solar power generation for up to 10 days (based on weather forecasting with 1-hour resolution)
  • The forecasting horizon will consist of 10x24=240 data points
  • Weather is described with 240xf data points (“f” being the number of weather features like temperature, humidity, etc.)
Figure 1: Preprocessed training data

Web Development:

The aim was to create a website where a user could register and get a precise prediction for their solar plant’s power outcome.

Figure 2: Branding

Frontend:

For the front end, we chose to use React. React is a commonly used JavaScript library for building fast and scalable frontends of websites. The creation of the react app was done with the help of a Youtube video from “JavaScript Mastery” [3]. Parts of the code were taken from the video; however, the structure and design were modified and extended to fit our needs. Our website consists of a landing page, a signup page, a login page, and a dashboard. For setting up the registration process and connecting it to the back end, a template was used [4].

Figure 3: Website Landing Page

Backend:

For the backend, we chose to use Django. Django is a Python web framework that has a Model-View-Template Architecture. The connection of the front- and backend was built up using a template [5] for a simple authentication system. Login, signup, and logout were already part of the template, but modifications had to be made to connect our backend and frontend successfully. Reasons were, amongst others, the usage of a newer Django version and the connection with we react files.

Figure 4: Prediction plot
Figure 5: Prediction table
Video: Demonstration of the website
  • Luise Weickhmann, Web Development
  • Tobias Küper, Data Science

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