Obviously, if the air filters in your home HVAC system are dirty, you should change them. But exactly how dirty is dirty? [Tim Rightnour] had heard it said that if you didn’t change your filter every month or so, it could have a detrimental effect on the system’s energy consumption. Thinking that sounded suspiciously like a rumor Big Filter™ would spread to bump up their sales, he decided to collect his own data and see if there was any truth to it.
There’s a number of ways you could tackle a project like this, but [Tim] wanted to keep it relatively simple. A pressure sensor on either side of the filter should tell him how much it’s restricting the airflow, and recording the wattage of the ventilation fan would give him an idea on roughly how hard the system was working.
Now [Tim] could have got this all set up and ran it for a couple months to see the values gradually change…but who’s got time for all that? Instead, he recorded data while he switched between a clean filter, a mildly dirty one, and one that should have been taken out back and shot. Each one got 10 minutes in the system to make its impression on the sensors, including a run with no filter at all to serve as a baseline.
The findings were somewhat surprising. While there was a sizable drop in airflow when the dirty filter was installed, [Tim] found the difference between the clean filter and mildly soiled filter was almost negligible. This would seem to indicate that there’s little value in preemptively changing your filter. Counter-intuitively, he also found that the energy consumption of the ventilation fan actually dropped by nearly 50 watts when the dirty filter was installed. So much for a clean filter keeping your energy bill lower.
With today’s cheap sensors and virtually infinite storage space to hold the data from them, we’re seeing hackers find all kinds of interesting trends in everyday life. While we don’t think your air filters are spying on you, we can’t say the same for those fancy new water meters.
The average person has become depressingly comfortable with the surveillance dystopia we live in. For better or for worse, they’ve come to accept the fact that data about their lives is constantly being collected and analyzed. We’re at the point where a sizable chunk of people believe their smartphone is listening in on their personal conversations and tailoring advertisements to overheard keywords, yet it’s unlikely they’re troubled enough by the idea that they’d actually turn off the phone.
But even the most privacy-conscious among us probably wouldn’t consider our water usage to be any great secret. After all, what could anyone possibly learn from studying how much water you use? Well, as [Jason Bowling] has proven with his fascinating water-meter data research, it turns out you can learn a whole hell of a lot by watching water use patterns. By polling a whole-house water flow meter every second and running the resulting data through various machine learning algorithms, [Jason] found there is a lot of personal information hidden in this seemingly innocuous data stream.
The key is that every water-consuming device in your home has a discernible “fingerprint” that, with enough time, can be identified and tracked. Appliances that always use the same amount of water, like an ice maker or dishwasher, are obvious spikes among the noise. But [Jason] was able to pick up even more subtle differences, such as which individual toilet in the home had been flushed and when.
Further, if you watch the data long enough, you can even start to identify information about individuals within the home. Want to know how many kids are in the family? Monitoring for frequent baths that don’t fill the tub all the way would be a good start. Want to know how restful somebody’s sleep was? A count of how many times the toilet was flushed overnight could give you an idea.
In terms of the privacy implications of what [Jason] has discovered, we’re mildly horrified. Especially since we’ve already seen how utility meters can be sniffed with nothing more exotic than an RTL-SDR. But on the other hand, his write-up is a fantastic look at how you can put machine learning to work in even the most unlikely of applications. The information he’s collected on using Python to classify time series data and create visualizations will undoubtedly be of interest to anyone who’s got a big data problem they’re looking to solve.
[Mastro Gippo] recently purchased a wall mounted charger for his electric car that looked great and had all the bells and whistles he wanted. There was only one problem: the thing burned up on him. Looking to find out how this seemingly high-end piece of hardware gave up the ghost so easily, he took it apart and tried to figure out where things went wrong. While he’s not looking to sling any mud and actually name the company who produced the charger, he certainly has some choice words for whoever green-lit this particular design.
With the charger open, there’s little doubt that something became very toasty inside. A large swath of the PCB has a black char mark on it, and in some places it looks like the board burned right through. After a close examination, [Mastro] is of the opinion that the board heated up to the point that the solder actually liquified on some connections. This conductive flow then shorted out components below it, and things went from bad to worse.
But where did all the heat come from? [Mastro] was stunned to see that a number of the components inside the charger were only rated for 30 amps, despite the label for the product clearly stating it’s good for up to 32A. With components pushed past their limits, something had to give. He wonders how such a device could have made it through the certification process; an excellent question we’d love to know the answer to.
The worst part is, it looks like the designers might have even known there was an overheating issue. [Mastro] notes that there are heatsinks bolted not to a component as you might assume, but directly to the PCB itself. We’ve seen what happens when designers take a cavalier attitude towards overheating components, and the fact that something like an electric vehicle charger was designed so poorly is quite concerning.
Data from 2016 pegs it as the hottest year since recording began way back in 1880. Carbon dioxide levels continue to sit at historical highs, and last year the UN Intergovernmental Panel on Climate Change warned that humanity has just 12 years to limit warming to 1.5 C.
Reducing emissions is the gold standard, but it’s not the only way to go about solving the problem. There has been much research into the field of carbon sequestration — the practice of capturing atmospheric carbon and locking it away. Often times, this consists of grand plans of pumping old oil wells and aquifers full of captured CO2, but there’s another method of carbon capture that’s as old as nature itself.
As is taught in most primary school science courses, the trees around us are responsible for capturing carbon dioxide, in the process releasing breathable oxygen. The carbon becomes part of the biomass of the tree, no longer out in the atmosphere trapping heat on our precious Earth. It follows that planting more trees could help manage carbon levels and stave off global temperature rises. But just how many trees are we talking? The figure recently floated was 1,000,000,000,000 trees, which boggles the mind and has us wondering what it would take to succeed in such an ambitious program.
Continue reading “A Trillion Trees – How Hard Can It Be?”
Greenhouses create an artificial climate specifically suited to the plants you want to grow. It’s done by monitoring conditions like temperature and humidity, and making changes using things like vents, fans, irrigation, and lighting fixtures to boost temperature. But how do you know when it’s time to up the humidity, or vent some of the heat building up inside? The easy way is to use the Arduino-powered Norman climate simulator from [934Virginia] which leverages data from different locations or times of year based on NOAA weather data to mimic a particular growing environment.
Norman relies on a simple input of data about the target location, working from coordinates and specified date ranges to return minimum/maximum values for temperature and humidity weather conditions. It makes extensive use of the Dusk2Dawn library, and models other atmospheric conditions using mathematical modeling methods in order to make relatively accurate estimates of the target climate. There are some simulations on the project’s Plotly page which show what this data looks like.
This data is used by [934Virginia’s] Arduino library to compare the difference between your target climate and actual sensor readings in your greenhouse. From there you can make manual changes to the environment, or if you’re luck and already have an Arduino-based greenhouse automation system the climate adjustments can be done automatically. The project is named after Norman Borlaug, a famous soil scientist and someone worth reading about.
Editor’s Note: This article has been rewritten from the original to correct factual errors. The original article incorrectly focused on replicating a climate without the use of sensors. This project does require sensors to compare actual greenhouse conditions to historic climate conditions calculated by the library. We apologize to [934Virginia] for this and thank them for writing in to point out the errors.
Images courtesy of Wikimedia Commons.
The first step to reducing the energy consumption of your home is figuring out how much you actually use in the first place. After all, you need a baseline to compare against when you start making changes. But fiddling around with high voltage is something a lot of hackers will go out of their way to avoid. Luckily, as [Xavier Decuyper] explains, you can build a very robust DIY energy monitoring system without having to modify your AC wiring.
In the video after the break, [Xavier] goes over the theory of how it all works, but the short version is that you just need to use a Current Transformer (CT) sensor. These little devices clamp over an AC wire and detect how much current is passing through it via induction. In his case, he used a YHDC SCT-013-030 sensor that can measure up to 30 amps and costs about $12 USD. It outputs a voltage between 0 and 1 volts, which makes it extremely easy to read using the ADC of your favorite microcontroller.
Once you’ve got the CT sensor connected to your microcontroller, the rest really just depends on how far you want to take the software side of things. You could just log the current consumption to a plain text file if that’s your style, but [Xavier] wanted to challenge himself to develop a energy monitoring system that rivaled commercial offerings so he took the data and ran with it.
A good chunk of his write-up explains how the used Amazon Web Services (AWS) to process and ultimately display all the data he collects with his ESP32 energy monitor. Every 30 seconds, the hardware reports the current consumption to AWS through MQTT. The readings are stored in a database, and [Xavier] uses GraphQL and Dygraphs to generate visualizations. He even used Ionic to develop a cross-platform mobile application so he can fawn over his professional looking charts and graphs on the go.
We’ve already seen how carefully monitoring energy consumption can uncover some surprising trends, so if you want to go green and don’t have an optically coupled electricity meter, the CT sensor method might be just what you need.
Continue reading “Building A Safe ESP32 Home Energy Monitor”
A frequent beginner project involves measuring soil moisture levels by measuring its resistance with a couple of electrodes. These electrodes are available ready-made as PCBs, but suffer badly from corrosion. Happily there is a solution in the form of capacitive sensor probes, and it is these that [Electrobob] is incorporating in to a home automation system. Unfortunately the commercial capacitive probes are designed to run from a 3.3 V supply and [Bob]’s project is using a pair of AA cells, so a quick hack was needed to enable them to be run from the lower voltage.
The explanation of the probe’s operation is an interesting part of the write-up, unexpectedly it uses a 555 configured as an astable oscillator. This feeds an RC low pass filter of which the capacitor is formed by the soil probe, which in turn feeds a rectifier to create a DC output. This can be measured to gain a reading of the soil moisture level.
The probe is fitted with a 3.3 V LDO regulator, which is simply bypassed. Measurements show its output to be linear, so if the supply voltage is also measured an accurate reading can be gleaned. These probes are still a slightly unknown quantity to many who might find a use for them, so it’s extremely useful to be given this insight into them.