Of all the things that I find would help immensely to reduce our reliance on condenser driers and help us to reduce our carbon footprints would be to monitor the weather and make suggestions about when might be a good time to put washing out and when to take it in. It's a simple concept but many people probably have no idea how much this might save them in energy bills and in their reliance on appliances.

According to a quick internet search: The average condenser tumble dryer uses about 5.2 kWh of electricity for a full load of laundry. Over the course of a year, the condenser dryer will use 617 kWh.

Additionally: There were an estimated 27.8 million households in the UK in 2020.

Multiplying out gives a total of 17.15TWh/year. This amounts to almost 5% of annual UK electricity generation based on a peak of 348.5TWh

A weather logger/predictor could reduce some of this wasted energy by helping people to make the most of outside weather to dry clothes the conventional, "natural," way.

Sometimes it's difficult to know how long washing will take to dry as that will depend on the weather at large. Wind speed, humidity, rainfall, sunshine and temperature will all have a part to play in determining speed of drying.

How might you know when it's likely to rain significantly and subsequently when should one put washing out or take it in?

The remainder of this article will discuss how to address these issues and optimise the process of drying clothes outside.

When drying clothes outside the following factors will probably have the greatest positive effect on how long the clothes will take to dry and whether it is sensible to leave them outside or take them in:

• High Air Temperature
• Low Humidity
• High Wind Speed
• High Sunshine Probability
• Low cloud cover percentage
• Low Rain Probability
• High Dew Point Temperature

Many weather web-providers  supply information of this nature. Data relating to the weather can be downloaded and processed by logging onto such servers as "Weather Underground."  in fact it may be possible to make a metric just by multiplying these indicators together initially. The eventual ideal would be to predict drying conditions based on the effect of prevailing weather conditions on the wet and dry weight of a patch of cloth hanging in the elements.

The patch of cloth can be weighed by a digital scale or strain gauge. Devices such as these are very common and relatively inexpensive. The weight of the patch of cloth can be monitored alongside the prevailing weather conditions in order to generate data regarding drying time and whether the weather is having a positive or negative effect on such drying time. 

The following image shows an arrangement for the Logger/Predictor. An RPI inside will communicate via a 3 core cable also supplying power to an ESP32 in an oil filled box. A lazy loop from the oil filled box will connect to the RPI and another will connect to a protecting housing for the strain gauge. A cloth will hang from the strain gauge. The strain gauge will measure the instantaneous weight of the cloth and send that to the RPi on the inside. The RPi will log weather data and weight of the cloth. As a cross reference some weights will be measured on e.g. a kitchen scales to calibrate the measurements for the cloth.

It is intended to send weights from a digital scales to a RPi SBC that will be able to log data from WUnderground into a database. An output from an ESP32 will allow backup data gathering if the SBC digital output fails.The digital scales will log weights of a patch of cloth that will then be representative of weights of laundry that have been subjected to the same environment. Gathering data from Wunderground that coincides with the weight data will allow a model to be generated linking environmental conditions to drying rates.

The RPi pins 39 GND and 40 GPIO 21 will be used to communicate with the ESP32 via 1-wire protocol.

To enable the one-wire interface you need to add the following line to /boot/config.txt, before rebooting your Pi:
dtoverlay=w1-gpio,gpiopin=21. See this link for more details.

The ESP32 will run communication via 1-wire using the OneWireSlave library it's worth noting that this doesn't seem to support ESP32 hardware by default. For learning about 1-wire this is an interesting site: http://playground.arduino.cc/Learning/OneWire. As a starter I decided to buy a few DS18B20 sensors and interface them to a Raspberry pi. The following page looked like a good starting point for this interfacing : https://www.circuitbasics.com/raspberry-pi-ds18b20-temperature-sensor-tutorial/

The DS18B20 is a temperature sensor but will be useful for proving the concept of interfacing via 1-wire protocol. If this is ever to become something that approaches a product it may be better it were designed as a completely wireless device.

For neural nets I have the following topic to read: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/