# Predicting Output Power for Nearshore Wave Energy Harvesting
## 1 Introduction
### Methods for Predicting Power Generation  
There are numerous methods for predicting the power generated from renewable energy sources. Support vector machines (SVMs) have been utilized to predict solar power by analyzing four years of historical satellite images [9]. The images were processed to extract a large number of input and output datasets for the SVM algorithm. The prediction model performed better than conventional time series methods. Fuzzy models that developed a prediction model from the relationship between the wave height and the power generation were proposed for forecasting electric power generation from ocean waves in Thailand [10]; the dataset for this experiment was collected from a video camera and a multimeter connected to the load. In the fuzzy modeling, the wave height is the input (cm), and electric power is the output (Watt). The video camera and a PVC pipe with a scale were used to measure the wave height, and a multimeter connected to a load of 300 W was used to measure the output power. Machine learning was used to forecast output power of photovoltaic (PV) systems based on four classes of weather condition [11]: clear sky, cloudy, foggy and rainy. Weather forecasting data and actual historical power output were used to develop a prediction model, which was tested on a real PV station; these proved to be effective.  
A comprehensive survey of machine learning applications in supporting renewable energy generation and integration that was presented by [PERSON], [PERSON] and [PERSON] [12] covered different techniques used to predict the output power from solar, wind and hydropower sources. Based on these studies, we can generalize that machine learning algorithms can be used to develop effective prediction models for renewable energy systems.  
There are several mechanisms to predict output power from wave energy converters including spectral analysis methods. However, the goal of this study is to establish a new method using machine learning algorithms, which can estimate the average power based on the movement data of the buoy. In this paper, we discuss our development of a wave power prediction model for the nearshore WEC made by INGINE. The model exploits machine learning algorithms to predict electric power generation from the movement of the buoys. The movement is monitored by an offshore module consisting of a tri-axial MPU6050 gyro-accelerometer sensor, a microcontroller and wireless transmitter that is attached to the buoy. This transmitter communicates with an onshore module that comprises a microcontroller and a wireless receiver. We analyzed the relation between the movement of buoys and the power generated by the WEC using machine learning algorithms, and we evaluated whether the model works for forecasting electric power generation. To this end, we applied several preprocessing techniques before classifying the collected data using supervised learning algorithms. We compared the performance of three classifiers: support vector machine (SVM), random forest (RF) and artificial neural network (ANN). The collected data were segmented, and each segment was labelled as weak, mild or strong in accordance with the amount of power generated. Based on the classification and the segmented data, we developed a regression model for the data to predict the power generation.  
Section 2 describes the working principle of the wave power harvesting device developed by INGINE. Section 3 explains the proposed approach and the experimental procedures. In Section 4, we explain the experiments that were conducted. Section 5 deals with the simulation result and includes a discussion of the work. In the final section, we offer our concluding remarks.