Energy systems need to evolve to meet the requirements of the modern world and the future. Hence, substantial effort is needed at an academic and industrial level to develop valuable diagnostic techniques. One current limitation of developing these diagnostic techniques is a lack of data on significant changes in the power signal (voltage and current). Producing sufficient data would require the deployment of data acquisition units that can record events of interest. These data acquisition units could be embedded systems deployed in the field. One way to detect changes in the signal is via a waveform prediction algorithm. Hence, waveform prediction will play a significant role in developing the trigger mechanism for monitoring algorithms as predicting a waveform a few cycles ahead allow the triggering of desired events. This thesis aims to find suitable machine-learning models for online and real-time time series prediction that predict the nominal state of a power signal waveform and could be used to detect significant events to activate a trigger mechanism in data acquisition devices. In this thesis, three different machine-learning models: Accurate Online Support Vector Regression (AOSVR), online Autoregressive Integrated Moving Average (ARIMA), and online Gated Recurrent Units (GRU) are implemented on a Raspberry Pi 3B+ and compared to each other in the aspects of computation time and error. Rigorous testing results demonstrate that the AOSVR had the lowest average operation in all the tested sampling rates, while the ARIMA had the overall lowest prediction time and lowest error when tested on data with sampling rates up to 2.5 kHz. Furthermore, all the models were able to produce a trigger at significant events. The results of this thesis could be used by the industry to further the development of diagnostic techniques, as well as increase the general knowledge of machine-learning applications on embedded systems.