A simple weather logger could help households reduce tumble-dryer use, cut energy bills, and lower carbon emissions.
A typical condenser dryer uses about 5.2 kWh per full load. Across millions of UK households, that adds up to a substantial national electricity demand each year. Even modest shifts to outdoor drying, when conditions are favorable, could produce meaningful savings.
Using 617 kWh per household per year and 27.8 million UK households, the implied total is 17.15 TWh/year, equivalent to about 4.9% of annual UK electricity generation (based on 348.5 TWh).
This project explores how weather data can guide better drying decisions: when to put washing out, when to bring it in, and when to avoid relying on weather at all.
A weather logger/predictor could reduce some of the wasted energy by helping people to make the most of outside weather to dry clothes the conventional, "natural," way.
This project explores how weather data can guide better drying decisions: when to put washing out, when to bring it in, and when to avoid relying on weather at all.
Key factors include:
- Air temperature
- Humidity
- Wind speed
- Rain probability
- Cloud cover
- Sunshine probability
By combining these inputs with measured drying results (for example, weight change over time), the system can estimate drying quality and provide practical recommendations.
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/