## 2 Related Works  
Over the years, there have been many developments in wind speed and direction sensing and making devices without rotating parts using ultrasonic or thermal technology has been the primary focus. In [20], a three-dimensional (3D) ultrasonic anemometer was developed based on the geometric placement of sensors. Based on the results, the system could estimate relatively higher wind speeds with good accuracy. Using micro-electromechanical systems (MEMS), the authors of [21] developed a low power ultrasonic anemometer using adaptive phase tracking. Additionally, in [22] a 3D ultrasonic anemometer is discussed using an ultrasonic sensor array that can provide accurate wind speed and direction measurements. Unlike ultrasonic sensors, thermal anemometers are based on the principle of heat exchange through interaction of a heated element with wind. Hot-wireanemometers [23; 24; 25] are popular thermal-type wind speed sensors that require minimal installation space and are portable for mobile operations. However, hot-wire anemometers can only measure wind/flow speed. In [26], a thermoelectric-type anemometer is developed that can measure wind speed with a high accuracy while keeping the power consumption ultra-low. Thermal anemometers have great potential as they can miniaturized, and due to their solid-state sensing capabilities, they require minimal maintenance. In [27], a solid-state anemometer is developed using thermal flow sensors that can, not only estimate the wind speed, but also the direction. Several research efforts on micro-machined two-dimensional thermal wind sensors can be found in [28], which focus on making solid-state anemometers smaller, more energy efficient and less sensitive to other environmental factors. Some major recent contributions to the wind speed sensing systems include the work in [29] which develops a piezoelectric wind speed sensor by establishing a mathematical relationship between wind velocity and a piezoelectric film's vibration frequency. A portable anemometer with spherical structure proposed in [30], which uses the differential pressure measurement across two non-intersecting tunnels to estimate fixed-point wind velocity and direction with a high degree of accuracy. In another development, a wearable anemometer is presented by the authors of [31], where wind velocity and direction are estimated through a thermal flow sensor, triaxial accelerometer and triaxial magnetometer. Reference [32] discusses the development of a miniature thermal wind sensor based on constant power control and the temperature distribution principle. Accurate wind speed measurements are important for reliable wind speed predictions and accurate wind energy assessment. Reference [33] introduces a method for offshore wind speed assessment using global wind speed datasets and in situ measurements. Most of the research on wind speed prediction on a short-term horizon relies on historical wind speed data. In [34], a short-term wind speed prediction model is proposed using variational mode decomposition (VMD) and a genetic algorithm ANN. The proposed model in [34] achieved a higher accuracy than other models in comparison. In [35], a hybrid evolutionary model was used to improve the accuracy of a long-term short-term memory neural network (LSTM) for short-term wind speed forecasting. In [36], a wind speed prediction method is introduced using ANN and wind speed measurements collected from multiple local sites. A stochastic optimization assisted ANN is used for short-term wind speed prediction in [37]. Apart from ANN, many other machine learning models have also been used for wind speed forecasting. In [38], a hybrid mode decomposition was used with an extreme learning machine in order to develop an outlier robust wind speed forecasting model. Reference [39] proposes a direct multistep wind speed prediction model by combining a self-adaptive data processing method with fuzzy entropy and a LSTM neural network.