As home automation starts to live up to its glossy sci-fi promise there remains a deficiency when it comes to interfacing between the newer computerised components and legacy items from a previous age. A frequent example that appears in projects on Hackaday is the reading of utility meters, and in that arena [jomjol] has a very neat solution involving an ESP32 camera module and a software neural network to identify meter readings directly.
The ESP and camera sit at the top of a 3D-printed housing that fits over the meter. The clever trick comes as each photo’s orientation is determined, and not only is OCR used to read digits but also figures are derived from small dial meters and other indicators on the meter face. It’s a very well-thought-out system, with a web-based configuration tool that allows full customisation of the readable zones and how they should be treated.
This project makes full use of the ESP32’s capabilities, and the attention to detail that has gone into making it usable is particularly impressive. It certainly raises the bar against previous OCR meter reading projects.
[Thanks for the tip Sascha]
In our info-obsessed culture, hackers are increasingly interested in ways to quantify the world around them. One popular project is to collect data about their home energy or water consumption to try and identify any trends or potential inefficiencies. For safety and potentially legal reasons, this usually has to be done in a minimally invasive way that doesn’t compromise the metering done by the utility provider. As you might expect, that often leads to some creative methods of data collection.
The latest solution comes courtesy of [Keilin Bickar], who’s using the ESP8266 and a serial TTL camera module to read the characters from the LCD of his water meter. With a 3D printed enclosure that doubles as a light source for the camera, the finished device perches on top of the water meter and sends the current reading to HomeAssistant via MQTT without any permanent wiring or mounting.
Of course, the ESP8266 is not a platform we generally see performing optical character recognition. Some clever programming was required to get the Wemos D1 Mini Lite to reliably read the numbers from the meter without having to push the task to a more computationally powerful device such as a Raspberry Pi. The process starts with a 160×120 JPEG image provided by a VC0706 camera module, which is then processed with the JPEGDecoder library. The top and bottom of the image are discarded, and the center band is isolated into blocks that correspond with the position of each digit on the display.
Within each block, the code checks an array of predetermined points to see if the corresponding pixel is black or not. In theory this allows detecting all the digits between 0 and 9, though [Keilin] says there were still the occasional false readings due to inherent instabilities in the camera and mounting. But with a few iterations to the code and the aid of a Python testing program that allowed him to validate the impact of changes to the algorithm, he was able to greatly improve the detection accuracy. He says it also helps that the nature of the data allows for some basic sanity checks; for example the number only ever goes up, and only by a relatively small amount each time.
This method might not allow the per-second sampling required to pull off the impressive (if slightly creepy) water usage data mining we saw recently, but as long as you’re not after very high resolution data this is an elegant and creative way to pull useful data from your existing utility meter.
There’s nothing quite as relaxing as a long, hot shower. This has the tendency of making the bather absent minded as to the amount of water being used, which can lead to excessive bills. [LiamOSM] built a device to monitor this instead, and calculate the cost, to boot.
The device consists of an Arduino hooked up to a cheap flow meter sourced from Banggood. The sensor consists of a paddle wheel that sits in the water flow, fitted with a magnet. A hall effect sensor picks up pulses as the magnet spins, and counting these allows the flow rate to be measured. An HD44780 LCD screen is used to display the readings, controlled over I2C.
To avoid issues in the bathroom environment, the enclosure was designed to be waterproof. The LCD is mounted behind a clear plastic window sourced from vegetable packaging, and the button chosen was specially selected for its sealing grommets. We’d love to see a proper submersion test, but for the most part, it appears to be doing a good job in the bathroom.
If you’re interested in monitoring your water use as a household, you might find it possible to piggy back on the municipal meter.
It’s no secret that hackers like to measure things. Good numbers lead to good decisions, like when to kick your wastrel teenager out of a luxuriously lengthy shower. Hence the creation of this wireless Arduino-based water meter interface.
We’ll stipulate that “wireless” is a bit of a stretch. Creator [David Schneider] chose to split the system into two parts – a magnetometer and an Arduino to sense impulses from the water company meter, and a Raspberry Pi to serve the web interface. The water meter is at the street rather than in his house, so the sensor is wired to the Pi with some telephone cable. But from there the system is wireless.
[David] goes into some good detail on the sensing problem he faced, which relies on detecting the varying magnetic field due to the spinny-bits inside the flowmeter and cleaning up the signal with the Arduino; he also addresses aliasing errors that occur when flow rate approaches the sampling rate of the magnetometer.
We like the fact that there’s a lot of potential to leverage this technique to monitor other processes with rotating magnetic fields. And like this optically coupled gas-meter monitor, it’s not invasive of the utility’s equipment either, which is a plus.
Electricity, Gas and Water – three resources that are vital in our daily lives. Monitoring them using modern technology helps with conservation, but the real impact comes when we use the available data to reduce wasteful usage over time. [Sébastien] was rather embarrassed when a problem was detected in his boiler only during its annual inspection. Investigations showed that the problem occurred 4 months earlier, resulting in a net loss of more than 450 cubic meters, equivalent to 3750 liters per day (about 25 baths every day!). Being a self professed geek, living in a modern “connected” home, it rankled him to the core. What resulted was S-Energy – an energy resource monitoring solution (translated) that checks on electricity, gas and water consumption using a Raspberry Pi, an Arduino, some other bits of hardware and some smart software.
[Sébastien] wanted a system that would warn of abnormal consumption and encourage his household folks to consume less. His first hurdle was the meters themselves. All three utilities used pretty old technology, and the meters did not have pulse data output that is commonplace in modern metering. He could have replaced the old meters, but that was going to cost him a lot of money. So he figured out a way to extract data from the existing meters. For the Electricity meter, he thought of using current clamps, but punted that idea considering them to be suited more for instantaneous readings and prone for significant drift when measuring cumulative consumption. Eventually, he hit upon a pretty neat hack. He took a slot type opto coupler, cut it in half, and used it as a retro-reflective sensor that detected the black band on the spinning disk of the old electro-mechanical meter. Each turn of the disk corresponds to 4 Watt-hours. A little computation, and he’s able to deduce Watt-hours and Amps used. The sensor is hooked up to an Arduino Pro-mini which then sends the data via a nRF24L01+ module to the main circuit located inside his house. The electronics are housed in a small enclosure, and the opto-sensor looks just taped to the meter. He has a nice tip on aligning the infra-red opto-sensor – use a camera to check it (a phone camera can work well).
Continue reading “Resource Monitoring Solution”