PVForecast Introduction

Forecasts to optimize electricity consumption for rooftop PV (photo-voltaic) installations

Table of Content

• Introduction
• PV Production Output Forecasts
• CO2 Intensity Forecast new
• Data Storage
• Sister Projects
• License and Disclaimer

Introduction

The project described here is developed in Python 3 and intended to run on Raspberries. It integrates with solaranzeige, which allows easy monitoring of a PV system.

• The Users Guide provides a full description of installation and configuration procedures.
• For ideas and questions, use the Discussions section on Github.
• If things don’t work as they should, raise an Issue in Github.

PV Production Output Forecasts

If one wants to forecast PV production output over the coming hours or days, two options exist:

• use a solar forecast provider which directly predicts output power for a predefined installation. The most prominent and accurate is probably Solcast
• use a weather forecast provider which also predicts solar radiation (GHI), or at the very least a cloud coverage estimation (at the cost of accurracy)
  • Deutscher Wetterdienst
  • Visual Crossing
  • Open Weather Map - only cloud based forecast, hence less accurate

This project supports all of the above. For the second group, modeling of the PV installation is required, which is done with the help of pvlib. Support for split array configurations (eg. east and west oriented panels) is provided.

See PV Output for more introductionary details

CO2 Intensity Forecast

new

In a perfect world, the PV rooftop installation would allow for a fully self-sufficient energy supply. Unfortunately, this is not the case: In winter and cloudy weather we have to rely on grid power.

But not all grid power is equal: Sometimes, the grid is supplied from wind (and hence at a low CO2 intensity), sometimes from coal or other carbon sources. Hence, it matters when heavy consumers (such as BEV charging or heat pumps) are operated. The CO2 intensity forecast capabilities support such decisions: It puts us in a position to select periods with a low CO2 footprint for such consumption.

See CO2 Intensity for more introductionary details

Data Storage

Independent of the data source - data wants to be stored. PVForecast supports a number of storage models:

• SQLite - a file based relation database
• Influx - a database optimized for time series storage
• Storage to csv files is also possible

Influx has undergone a major, largely not backward compatible upgrade between version 1.x and 2.x. Luckily, both version are supported by PVForecast

Influx storage will overwrite any forecasts with newest values, whereas SQLite will store all forecast horizons. This is useful to research forecast accuracy.

Sister Projects

• PVOptimize optimizes energy usage produced by a rooftop PV system
• PVControl is a GUI to control PVOptimize

License and Disclaimer

Distributed under the terms of the GNU General Public License v3

The software pulls data from various weather sources. It is the users responsibility to adhere to the use conditions of these sources.

The author cannot provide any warranty concerning the availability, accessability or correctness of such data and/or the correct computation of derived data for any specific use case or purpose. Further warranty limitations are implied by the license.