A phase estimation algorithm is presented to estimate the phase of a recurring pattern in a nonstationary signal. The signal is modelled by a template signal that represents one revolution of the recurring pattern, and that the frequency of this pattern can change at any time with no assumptions about local stationarity. The algorithm uses a constraint maximum likelihood estimator (MLE) to estimate the phase of the recurring pattern in the time series. Using the dynamic programming techniques from the dynamic time warping (DTW) algorithm, the solution is found in an efficient manner. The algorithm is applied to the digitization of meter readings from analog consumption meters.

As of today, analog consumption meters are still widely used to measure the consumption of gas, electricity and water. Often, smart home appliance use a simple reflective photosensor located on a rotating part of the meter to obtain information about the state of the consumption meter. The algorithm presented in this thesis accurately estimates the phase of the repeating pattern that occurs in the sensor observation when the meter rotates. Using this estimate, the signal of the photosensor can be converted to an estimate of the total resource consumption and consumption rate.

The algorithm improves in accuracy over conventional methods based on peak detection, and is shown to work in cases where the peak detection methods fails. Examples of this are signals where there is no distinctive peak in the signal or a signal where the recurring pattern is reversed. Furthermore, a template compression scheme is proposed that is used to decrease the computational complexity of the algorithm. Different time series compression methods are applied to the algorithm and evaluated on their performance.

With the arrival of smart electricity meters energy consumption can be monitored continuously and displayed on external devices such as phones or tablets. As a consequence, users become more aware of their energy usage which may result in a reduction in energy consumption. Nevertheless, many countries in Europe still use an analog electricity meter based on a rotating disk, which rotates at a speed proportional to the consumption passing through the meter. In contrast to a smart meter it is not possible to read out the consumption directly. As an alternative, the rotating disk can be observed such that the power (W) and Energy (kWh) consumption can be derived. The current sensor device developed by Quby, uses an LED and phototransistor, where the LED emits light on the disk that is reflected towards the phototransistor. Because of the physical properties of the disk, the sampled phototransistor signal can be represented as a pulse signal, where each pulse indicates a revolution of the disk. The sensor device is mains powered, however, many electricity meters are not located near a power outlet requiring the sensor device to be battery powered. This is a challenging problem since the LED has a relatively large power consumption. The pulse-detection algorithm running on the mains-powered sensor device assumes an LED current of 10 mA with a sampling frequency of 10 kHz. Quby requires that the battery-powered device will last at least one year on an energy budget of 4200 mAh (roughly four AA batteries), and the percentage error of the determined energy consumption should be less than 5%. This implies a factor 50 in power reduction. In this thesis we propose an energy-efficient noise-robust pulse-detection algorithm to detect pulses while keeping the LED current to a maximum of 1 mA. To preserve more energy, the LED is duty cycled to at most 20% instead of 100%, and the sampling frequency is reduced to a maximum of 400 Hz. The proposed method is based on a statistical model where pulse detection is used by means of a multiple-sample likelihood ratio test. Due to the low LED current the signal statistics, such as pulse amplitude, offset and noise, are very sensitive to ambient light. Therefore, an additional method is proposed to estimate these statistics continuously. As a consequence, the detection thresholds in the likelihood ratio test are dynamically adjusted based on predefined probabilities of a false alarm and true detection. The proposed algorithm is extensively tested in a lab setup with three different analog electricity meters, a varying load and a light source emitting light in the same relative spectral-power distribution as sunlight. Moreover, measurements are performed at two households for one week each. From the experiments it can be concluded that the proposed method can last for at least one year when battery-powered, while predicting energy consumptions with an error of less than 2%.