## 1 Introduction  
The popularity and necessity of renewable energy is creating the demand for residential hybrid renewable energy systems (HRES). Renewable energy is not only essential for reducing pollution but also in order to satisfy the ever-increasing demand for energy. To efficiently manage energy generated HRESs, real-time monitoring of wind speed is required by many renewable energy related systems. Wind speed is crucial for computing/estimating wind power output [1, 2], as well as solar power output [3, 4]. Commercial wind speed sensors are mainly of three types: (a) rotary/cup anemometer, (b) hot-wire anemometer and (c) ultrasonic anemometers. Although, each of these types has different working principles, their market prices are notably high for residential usage applications. Wind speed data are broadly used for wind energy potential analysis, as presented in [5, 6, 7]. Similarly, wind speed data are also used for optimal sizing of renewable energy systems [8, 9, 10, 11], energy management systems relying on weather forecasts [12, 13, 14, 15]. The wind speed and wind direction data are invaluable for renewable energy potential analysis and renewable energy generation applications. However, many challenges regarding current technologies for measuring the wind speed and wind direction include:  
* Rotating parts in cup anemometers and wind vanes are prone to degradation and faults over time and result in inaccurate readings. * Many applications are constrained by size, and a wind flow trajectory may not be suitable for rotating anemometers. * In order to encourage the use of such sensors in residential setups, the cost of a system should be affordable. Costs of wind speed and direction measurement systems are highly influenced by the components used for sensing the wind attributes. Therefore, technology that is more sophisticated may provide a higher accuracy but may lead to significantly higher system costs. Several studies on the development of anemometers rely on expensive and complex manufacturing processes. However, such systems are not accessible to general/residential users due to the initial capital restrictions. In this study, we developed a low-cost high-accuracy wind speed and direction sensing system using a thermopile array, a positive temperature coefficient (PTC) heating element and a heat sink. The heat sink is used as a heat exchange between the PTC heater and the environment, i.e., wind behavior. An independent temperature sensor is used for collecting the ambient temperature data as changes in ambient temperature influence sensing accuracy of thermal anemometers [16; 17]. The major contributions of this study are:  
* This study reports on the development of a low-cost wind speed and direction sensing system using easily available and low-cost components, including a thermopile array, a PTC heater, a heat sink, and an ambient temperature sensor. * The proposed device does not have any rotating or moving parts, hence, it requires less maintenance and can transfer measurement data wirelessly without a need for wires. * The use of an artificial neural network (ANN) to estimate the wind speed and direction from the thermal distribution using data collected from the thermopile array and ambient temperature sensor with a high coefficient of determination (R\({}^{2}\) score) at different sampling intervals. In order to evaluate the accuracy of our system, we compared the wind speed and direction estimated with respect to a cup anemometer and a wind vane, respectively. We use support vector machine (SVM) [18] and linear regression (LR) [19] with least squares to compare the estimation accuracy of wind speed and direction using the thermopile data against an ANN method. Selection of SVM and LR methods for comparison is based on their applicability and popularity for use in signal processing, regression and prediction tasks, as discussed in Section 2.