Assessment of Single-Axis Solar Tracking System Efficiency in Equatorial Regions: A Case Study of Manta, Ecuador

Abstract

Ecuador is grappling with a severe energy crisis, marked by frequent power outages. A recent study explored solar energy efficiency in the coastal city of Manta using an IoT real-time monitoring system to compare static photovoltaic (PV) systems with two single-axis solar tracking systems: one based on astronomical programming and the other using light-dependent resistor (LDR) sensors. Results showed that both tracking systems outperformed the static PV system, with net gains of 31.8% and 37.0%, respectively. The astronomical-programming-based system had a slight edge, operating its stepper motor intermittently for two minutes per hour, while the LDR system required continuous motor energization. The single-axis tracker using astronomical programming demonstrated notable advantages in energy efficiency and complexity, making it suitable for equatorial regions like Manta. The study also suggested potential further gains by adjusting solar positioning at shorter intervals, such as every 15 or 30 min. These findings enhance our understanding of solar tracking performance in equatorial environments, offering valuable insights for optimizing solar energy systems in regions with high solar radiation. By emphasizing customized solar tracking mechanisms, this research presents promising solutions to Ecuador’s energy crisis and advances sustainable energy practices.

1. Introduction

In recent years, studies have also been carried out in which the improvement in efficiency in dual-axis solar tracking was achieved using an adaptive algorithm to mitigate solar dispersion. A dual-axis solar tracking system is proposed that significantly enhances solar monitoring efficiency and precision, demonstrating a 65% increase in efficiency compared to manual methods [1]. Dual-axis solar trackers utilizing trajectory calculations with navigation sensors yield 67.65% more energy. The study emphasizes the impact of design, location, and climate on tracking efficiency [2]. In other studies, only a percentage increase in energy of 29.80% has been achieved [3]. Additionally, the passive cooling of high-concentration triple-junction solar cells is investigated through different heat sink configurations, resulting in notable improvements in thermal performance [4]. The energy, economic, and environmental analysis of photovoltaic panels in single-axis and dual-axis tracking systems, reported in the third article, reveals significant benefits compared to fixed systems [5]. Likewise, an experimental study of passive cooling for high-concentration triple-junction solar cells using specific heat sink configurations shows positive outcomes in enhancing thermal performance [6]. A new approach is introduced for monitoring solar heat intensity in dual-axis tracking systems, employing advanced technology that significantly enhances control system efficiency and precision [7]. These advancements are crucial to ensuring energy stability and promoting sustainable development in regions vulnerable to climate change.

The necessity of identifying sustainable and efficient energy solutions in equatorial regions is underscored by the ongoing energy crisis in Ecuador and Colombia. This crisis is marked by frequent nationwide power outages attributed to prolonged droughts and heavy reliance on hydroelectric power [8,9]. Hydroelectric-dependent countries face significant energy challenges exacerbated by climate change. Shifts in precipitation patterns and glacier melt affect water availability, disrupting hydropower generation. An increased frequency of extreme weather events such as droughts and floods further strains hydroelectric infrastructure, highlighting the urgent need for diversified energy sources and robust climate adaptation strategies. Photovoltaic solar energy emerges as a promising alternative in diversifying the energy matrix of these countries; however, some challenges and obstacles need to be addressed for it to become more competitive with other energy sources. Two specific concerns often raised are high production costs and low-efficiency conversion [10,11]. Regarding production cost, significant advancements in PV technology and economies of scale in production have led to a substantial decrease in costs over recent years. The cost of PV panels continues to decline, making solar energy more economically viable as a long-term energy solution to replace traditional energy sources [12,13]. On the other hand, low efficiency relates to the technology and process of converting solar energy into usable electrical energy. Crystalline silicon (c-Si) wafer-based technology is the most widely used worldwide due to its low cost and high availability in nature; however, its maximum energy conversion reaches only 26% at best [14]. Other emerging technologies with higher efficiency face challenges in scaling up the manufacturing processes for new photovoltaic materials, addressing manufacturing complexity, and reducing costs to achieve large-scale production [15].

Another historical approach to increase the efficiency of PV systems considers the use of solar tracker systems. By continuously orienting the solar panels to face the Sun throughout the day, solar trackers maximize the amount of solar irradiation that the PV panels receive. This enhances the electricity production of the PV system [16,17]. However, it is important to note that the implementation of solar trackers may add complexity and additional costs to the PV system, which must be considered in the overall economic and energetic analysis. According to [18], while solar tracking systems can have significant benefits in any location, certain regions may have specific characteristics that amplify the advantages of such systems, for instance, those located far north and south of the equatorial regions where most studies have been carried out. In some countries, the use of solar tracking systems can improve electricity generation by around 30% to 80% compared with fixed solar systems [17,19]. On the contrary, there are very few studies focused solely on sun-tracking systems in equatorial regions [18]. In this region, the amount of solar radiation received through the years is generally high, with a relatively stable Sun trajectory and minimal seasonal variations [20]. As a result, the potential benefits of solar tracking systems in terms of increased energy production may not be as pronounced compared to regions with higher latitudes. However, it is worth noting that there are still advantages to implementing solar tracking systems in equatorial regions. For instance, a comparative simulation-based experiment in Ecuador between single- and dual-axis tracking PV systems vs. fixed PV systems found improvements of 27.3% to 30% and 31.2% to 34.62%, respectively [10,21]. Some other experimental studies in Ecuador and Brazil found that these energy improvements could be lower than those in simulation-based experiments. In Ecuador, a dual-axis PV system proved to be only 19.62% more efficient than a fixed PV system [18]; in Brazil, a single-axis tracking PV system showed an average enhancement of 11% over a fixed PV system [10]. As can be seen, there are certain discrepancies between simulated and experimental data regarding sun-tracking solar systems in equatorial regions. While it has been possible to compare the simulated and experimental information within the same country for dual-axis PV systems, it is still necessary to delve deeper into experimental data concerning single-axis PV systems.

This work is a follow-up to a previous study [18] to fill the gap in knowledge regarding single-axis solar tracking systems operating in equatorial regions, taking the city of Manta as a case study. This study focuses on the performance of two types of horizontal single-axis tracking systems, with the axis aligned in the north–south direction. Specifically, it aims to design and analyze a static photovoltaic system alongside two single-axis solar tracking systems: one using astronomical programming and the other employing LDR sensors. The objective is to quantify the improvement in energy efficiency of the systems for each proposal.

This innovative proposal aims to quantify, through a one-month trial in August, the real-time energy monitoring of the system and the improvement in energy efficiency resulting from this proposal. This will shed light on the actual performance of solar tracking systems in these regions, especially in simpler tracking proposals compared to two-axis trackers. This addresses Ecuador’s energy crisis and fosters energy resilience.

2. Solar Tracking Classification

Determining the optimal placement of photovoltaic (PV) generators involves considering the available solar resources and meteorological conditions. Various studies have designed algorithms to calculate the optimal tilt angle of PV generators using artificial intelligence techniques, which have been tested experimentally to enhance efficiency [22]. Other studies use simulations to determine the best placement of generators, minimizing cloud cover effects [23], or design covers that optimize the distribution of radiation on panels, increasing energy reception at all incidence angles [24]. These works complement studies that propose tracking methods to improve the performance of photovoltaic systems [25].

Solar tracking systems are broadly categorized into passive and active types. Passive systems do not require external power and rely on manual adjustments or inherent physical movements, while active systems, powered by electrical energy, automatically track the Sun’s position using microprocessors, motors, gears, and sensors. Active systems are more precise than passive tracking systems but also more complex and costly. Active tracking systems are further divided into three categories: control-drive-based systems, which optimize the orientation of solar panels to maximize energy capture; closed-loop and open-loop tracking systems, where the former use feedback control mechanisms to continuously monitor the position of the Sun while the latter rely on preprogrammed algorithms or schedules to determine the Sun’s position and adjust the solar panel orientation accordingly; and, finally, hybrid systems, which combine open-loop and closed-loop control systems [19].

An alternative and more common classification is based on the degree of freedom (DOF) of the tracking systems: single-axis, dual-axis, and fixed-tilt solar arrays [26]. Among these, dual-axis tracking systems can adjust both the horizontal (azimuth) and vertical (elevation or inclination) axis; in contrast, single-axis systems typically rotate along either the horizontal axis (east to west) or the vertical axis (north to south); these are the most common solar tracking systems [27]. The different types of angles and axes that play an important role in these tracking systems are (see Figure 1) zenith angle (z), altitude angle (αs), declination angle (δ), angle of incident (i), azimuth angle (γ), and inclination angle (β) [28].

Single-axis tracking systems capture more energy than fixed solar installations by better aligning panels with the Sun. They are more cost-effective and simpler to implement than dual-axis systems. These systems are often used in utility-scale solar farms, where maximizing energy output is crucial, but the complexity and cost of dual-axis tracking systems may not be justified. According to [27], this system is divided into three different types: horizontal single-axis tracking system (HSAT), vertical single-axis tracking system (VSAT), and tilted single-axis tracking system (TSAT). Figure 2 presents all types of single-axis solar tracking systems.

In this ongoing quest to enhance the efficiency of solar energy capture, the orientation and tracking of photovoltaic (PV) panels play a crucial role.

Table 1. Studies with different types of tracking simulations and experimental prototypes.

Methodology	Study	Key Findings	Location
Simulated	[30]	30.4–34.6% efficiency improvement in the dual-axis tracking PV system compared to simulations on a fixed PV system.	Turkey
	[31]	31.29% efficiency improvement in the dual-axis tracking PV system compared to simulations on a fixed PV system.	Jordan
	[32]	1.86–31.52% efficiency improvement in the two-axis tracking PV system compared to simulations on an optimally tilted PV system	Nigeria
	[10,21]	27.3–30% and 31.2–34.32% efficiency improvement in the single-axis and dual-axis tracking PV system, respectively, compared to simulations on a fixed PV system	Ecuador
	[33]	28.91% and 33.11% efficiency improvement in the single-axis and dual-axis tracking PV system, respectively, compared to simulations on a fixed PV system	Kazakhstan
	[34]	9.16–34.6% efficiency improvement in the dual-axis tracking PV system compared to simulations on a fixed PV system.	Malaysia
Experimental Prototype	[1]	The performance of advanced dual-axis solar trackers is 41% superior to that of traditional two-axis trackers.	Almaty—Kazakhstan
	[10]	11% range on cloudy days vs. fixed 20% on sunny days based on photovoltaic cell material.	Brazil
	[18]	19.62% comparison fixed between fixed and dual-axis sun tracking	Ecuador
	[19]	Controlled by a microcontroller and LDR, is 13% more efficient than fixed panels.	India
	[35]	Comparison: the hybrid-controlled dual-axis solar tracking system is 23.3% more efficient than fixed solar systems.	Egypt
	[36]	A dual-axis solar tracking system with mirror reflection boosts performance by 108.36% compared to stationary panels.	Malaysia
	[37]	25% improvement with respect to the single-axis of 40% in comparison to fixed solar panels	India

has been constructed to illustrate this. Various studies have approached the optimization of these systems through different methodologies, ranging from simulations to the design of experimental prototypes located in countries such as Turkey and Jordan, among others [30,31].

The improvement in the energy efficiency of PV systems using solar tracking mechanisms is considerable compared to fixed systems, with increases ranging from 1.86% to 108.36% depending on geographical and climatic conditions [1,18,19,29,30,31,32,33,34,35,36,37]. These results highlight the importance of adopting solar tracking technologies to optimize PV energy production and improve the economic viability of solar projects globally. Given these findings, our solution stands out as it not only enhances efficiency but also provides a cost-effective alternative, addressing the challenges of the energy crisis prevalent in our country, making it a promising solution for sustainable energy production.

3. System Realization and Experimentation

This research was carried out in the facilities of the Universidad Laica Eloy Alfaro de Manabí, in Manta City, and serves as a continuation of our previous study on two-axis solar tracking systems [18]. Our previous two-axis monitoring system achieved significant energy gains of approximately 19.62%, but its complexity and higher energy consumption presented challenges. During this study, although significant energy gains were achieved, they were lower than the gains reported in simulated experiments for equatorial regions, which have shown increases of up to 31.2% [38]. The primary issues identified included an inadequate mechanical positioning system that struggled to withstand constant winds and difficulties in accurately positioning the panels on cloudy days, which negatively impacted the system’s overall energy efficiency.

Given these findings, it is evident that the choice between two-axis and single-axis solar tracking systems is crucial for optimizing solar energy harvesting, particularly in equatorial regions like Manta, Ecuador. To address these challenges, we propose a new experimental study focused on single-axis solar trackers, specifically utilizing light-dependent resistors (LDRs) and astronomical programming. This study aims to corroborate the results of simulated experiments conducted by other authors with technical research, enhancing our understanding and implementation of efficient solar tracking systems in equatorial climates [18]. Figure 3 shows the project assembled with each of the system components placed in an open area to avoid shadows. The block diagram of the proposed system is shown in Figure 4. It is composed of the following parts: an automatic weather station, two single-axis sun-tracking systems (based on light-dependent resistors (LDRs) and astronomical programming), one fix-tilt solar system, and a data acquisition and monitoring system module.

3.1. Automatic Weather Station

The IoT-based automatic weather station (AWS) is an economical solution equipped with various instruments to gather and log meteorological parameters such as solar radiation, UV radiation, wind speed and direction, temperature, and humidity. A datalogger (based on Raspberry Pi3 (Raspberry Pi Foundation, Cambridge, UK; purchased from Megatronica, Quito, Ecuador)) is responsible for the collection and recording of all meteorological measurements and also for processing and transmitting the data via the Ethernet network to our database. The AWS provides solar radiation and temperature references for the whole system. The real-time data history is accessible to the university community through a web platform. For further details, refer to [39].

3.2. Fix-Tilt Solar System

The fixed-tilt solar system is depicted on the front side of Figure 3. This system features a monocrystalline photovoltaic (PV) panel with a maximum power output of 120 Wp, a short-circuit current of 7.20 A, and an open-circuit voltage of 22.5 V. The panel operates with a standard test condition (STC) efficiency of 18.46%. The stationary structure of this system is set at a 5° inclination, aligning with latitude-based measurements as detailed in [10]. This same PV panel is employed in both the single-axis tracking system with LDR sensors and the single-axis tracking system with the astronomical programming system. Detailed characteristics of the PV panel are provided in [40].

3.3. Single-Axis Tracking System Structure

The structures of the two single-axis solar tracking systems have been designed identically to compare the performance under equal technical conditions. The movement is carried out using an electric stepper motor in conjunction with a power screw. This converts the rotational movement into linear movement, giving greater strength and precision to the movement of the solar panel. The base of the structure is a single post fixed to the installation site. Figure 5 shows part of the structure.

The stepper motor chosen is a Nema 23 57HS41-2006 (Stepperonline; purchased from Megatronica, Quito, Ecuador) [41], which meets the design requirements to raise and lower the solar panel placed on the axis of rotation (torque greater than 0.08425 Nm). This motor is commanded by a TB6600 stepper motor driver controller (Sorotec; purchased from Megatronica, Quito, Ecuador) [42], and this, in turn, is connected and controlled by the Arduino Mega Module (Arduino S.R.L., Torino, Italy; purchased from MegatronICA, Quito, Ecuador).

To control the solar tracker system, the linear movement of the power screw must be correlated with the degrees of freedom established for the structure. The solar tracker system has 130° of freedom (δ = 130°) and, for its most extended position, the power screw has a length of 29 cm; on the contrary, for its most retracted position, its length is 14.5 cm. Figure 6 shows the representation of the movement of the solar tracking system and the measurements of the oblique triangle formed by the power screw and the structure.

To determine the linear movement per degree of inclination, we examined the variation in displacement resulting from a 1° change in angle δ (129°). By analyzing the data presented in Figure 6, we calculated that the linear movement per degree of inclination is 0.6 mm. This calculation was based on the displacement difference between the initial and recalculated distances. Considering that the power screw has a pitch of 1.5 mm, it was concluded that, for each complete turn of the stepper motor, the panel will tilt 2.5°.

3.4. Single-Axis Tracking System Based on LDRs

This is an active tracking system based on a closed-loop control circuit. The tracking system uses a microprocessor and two photoresistors or photocells (LDRs). The photoresistor’s resistance is inversely proportional to the light intensity falling on it, so it has very high resistance in darkness (≈1 MΩ) but it decreases to a few kΩ once it receives light (see Figure 7). The output signals from the LDRs are sent to the microprocessor, where they are digitized and compared continuously to determine the position of the Sun. Next, a signal proportional to this difference is sent so that the stepper motor executes the positioning of the solar panel perpendicular to the estimated position of the Sun. Figure 7a shows a representation of the operation of the LDRs and Figure 7b shows the LDRs measurement system and the LDRs.

3.5. Single-Axis Tracking System Based on Astronomical Programming

This is an active tracking system based on an open-loop control circuit. The tracking system uses a microprocessor and a control algorithm. This system tracks the Sun without physically following the Sun through sensors. Instead, it uses a solar chart to calculate the solar angles of the Sun. The solar chart is a table for a specific place on Earth that, according to the day of the year and the time, indicates the position of the Sun using solar coordinates. There are numerous web tools used to determine the solar chart, such as those shown in [43]. To determine each specific time of the day, a real-time clock (RTC Arduino module) is used to provide the information needed at all times. Figure 8 shows the simulation of the Sun path diagram for the selected location and

Table 2. Sun path coordinates. Adapted from [44].

Time of the Day	Summer Solstice	Winter Solstice	Spring Equinox	Autumn Equinox	Average Angle
6:00	−4.4°	−6.1°	−3.8°	−7.7°	−5.5°
7:00	9.5°	7.8°	11.2°	7.5°	9.0°
8:00	23.1°	21.4°	26.2°	22.4°	23.3°
9:00	36.5°	34.7°	41.2°	37.4°	37.5°
10:00	49.1°	47.2°	56.2°	52.4°	51.2°
11:00	60.2°	58.1°	71.2°	67.4°	64.2°
12:00	67°	64.9°	86.1°	82.4°	75.1°
13:00	65.6°	64.1°	78.8°	82.5°	72.8°
14:00	57.1°	56.4°	63.8°	67.4°	61.2°
15:00	45.4°	45.1°	48.8°	52.6°	48.0°
16:00	32.5°	32.4°	33.8°	37.6°	34.1°
17:00	19°	19°	18.8°	22.6°	19.9°
18:00	5.4°	5.4°	4°	7.7°	5.6°

shows the Sun coordinates throughout the year.

As can be observed in Figure 8 and Table 2, the Sun’s path in equatorial latitudes presents a special profile where there is very little variation in the Sun’s path during the year. For this reason, the average angle for the entire year was taken as a reference; these data were then correlated with the degrees of freedom established for the single-axis solar tracker to finally send the corresponding command to the stepper motor to execute the movement of the solar panel hourly. For the azimuth angle, a fixed east–west orientation was taken.

3.6. Sensing System

Based on what was presented in [18], three equal sensing systems were deployed to evaluate the efficiency of each system. The voltage, current, and temperature of each solar system were evaluated. Temperature measurements were recorded at one-minute intervals, while voltage and current measurements were obtained consecutively at a frequency of 30 samples per minute. These values were averaged to produce a single instantaneous measurement per minute. To estimate the maximum power point (MPP) from these measurements, we employed the Araujo–Green method [25,45,46]. These minute-by-minute power values were then averaged to calculate the hourly average power output. The total daily energy generated was determined by summing the hourly average power outputs over the course of one day. The structure and characteristics of these sensing systems are detailed below:

• Current and voltage sensing system: To measure voltage and current values, a set of relays was employed to control the acquisition of short-circuit current (I_Sc) and open-circuit voltage (V_oc) data for each solar system (see Figure 9). Voltage is measured using the FZ0430 circuit module [47]. This circuit is a straightforward voltage divider with values of 30 kΩ and 7.5 kΩ. Essentially, it implies that when the maximum input voltage is attained, the voltage sensed by the module is divided by a factor of five. The maximum resolution acquired is 4.89 mV (5 V/1023) since the Arduino Mega ADC has a 10-bit resolution. For current detection, the ACS712ELC 20 A module was employed [48]. This sensor produces an analog voltage output signal that changes proportionally with the detected current, whether it is alternating or continuous, up to 20 A. The sensor is composed of a precise, low-offset, linear Hall-effect sensor circuit featuring a copper conduction path (internal resistance 1.2 mΩ) situated close to the die’s surface. It operates at 5 VDC, (66 mV/A) centered at 2.5 V (Vcc/2), exhibiting a typical error of 1.5%.

• Temperature sensing system: One of the most relevant factors that affect the efficiency of the solar panel is the operating temperature. As the temperature increases, the efficiency decreases due to the negative temperature coefficient of power in PV modules. The standard testing condition (STC) temperature is 25 °C, and deviations from this temperature impact the output power. Specifically, when the temperature exceeds 25 °C, the output power is lower than the maximum value specified by the manufacturer, while temperatures below 25 °C can lead to increased power output [49,50]. To verify and compare the working temperature of the solar panels, three LM35 sensors were used on each of the solar panels. This sensor is a highly accurate integrated circuit with an output voltage that linearly corresponds to the instantaneous temperature in Celsius degrees. Its temperature range spans from −55 °C to +150 °C, and it operates with a scale factor of 10 mV/C.

4. Result and Discussion

According to [18], the fact that Ecuador does not have four seasons and there are no extreme weather conditions throughout the year (only cloudy and sunny days in the summer and sunny and rainy days in the winter) allows us to undertake the study by considering a representative period. Given this uniformity, our analysis focused on a representative month, August. We found no significant differences in solar panel performance between these periods, as the sunlight reaches the equator almost perpendicularly throughout the year. Thus, the selected month adequately represents the typical operating conditions for a single-axis solar tracking system in equatorial regions. This approach ensures a comprehensive understanding of system performance without redundancy.

The system’s performance was monitored from 6:00 a.m. to 6:00 p.m., with measurements taken at one-minute intervals each day to obtain temperature, short-circuit current (I_sc), and open-circuit voltage (V_oc). Measurements of solar radiation in W/m² were taken by the AWS, and were used as a control element for the energy produced by the solar systems. Apart from the data obtained from the three solar systems, to complement this study, a record of the state of the sky was made according to cloudiness: sunny, partly cloudy, and cloudy (through qualitative observation). With the evaluation carried out, there are enough data to provide a representative sample of the behavior of the three systems.

4.1. Systems Comparison Performance

For this analysis, one of the months studied will be taken as an example, and the behavior of the tracking and fixed systems will be presented based on measured solar radiation data to ensure objective classification. Days will be grouped by the daily solar radiation received by the horizontal surface, as measured by the pyranometer of the weather station. Specifically, we categorized the days into three groups based on daily solar radiation: low-radiation days (1100–2500 Wh/m²), medium-radiation days (2501–4300 Wh/m²), and high-radiation days (4301–6100 Wh/m²). Figure 10 and Figure 11 compare the system performance of the two single-axis tracking PV systems with the static PV system on a high-radiation day while Figure 12 and Figure 13 show the same comparison on a medium-radiation day and Figure 14 and Figure 15 on a low-radiation day. These figures specifically display the hourly energy production of each system, highlighting how solar radiation influences their performance depending on sky conditions.

Table 3. Systems’ average performance.

	S-A_TPV1 *		S-A_TPV2 **		S PV ***		S PV vs. S-A_TPV1	S PV vs. S-A_TPV2
Day	Energy (Wh)	Temp (°C)	Energy (Wh)	Temp (°C)	Energy (Wh)	Temp (°C)	Gain1 (%)	Gain2 (%)	Radiation Wh/m2	Classification
1	463.5	29.9	491.5	31.2	373.6	29.5	24.1	31.6	3462.6	Medium radiation
2	573.2	29.4	592.1	30.9	421.3	30.0	36.1	40,6	3378.6	Medium radiation
3	173.0	24.3	182.4	24.4	172.7	23.3	0.2	5.7	1182.2	Low radiation
4	628.1	30.1	649.7	30.8	524.0	28.5	19.9	24.0	2729.0	Medium radiation
5	283.3	28.7	292.4	28.5	239.9	27.9	18.1	21.9	2040.8	Low radiation
6	487.4	32.7	516.2	37.8	347.4	26.7	40.3	48.6	3054.1	Medium radiation
7	434.6	30.3	455.8	32.5	353.7	27.1	22.9	28.9	2756.0	Medium radiation
8	451.0	30.8	480.6	31.5	382.3	29.1	18.2	25.7	2625.3	Medium radiation
9	334.6	28.6	350.4	29.0	284.0	27.3	17.8	23.4	2634.0	Medium radiation
10	370.6	29.1	393.2	29.9	298.8	27.4	24.0	31.6	2798.7	Medium radiation
11	641.7	31.2	667.8	32.2	494.1	29.2	29.9	35.2	5074.6	High radiation
12	875.0	32.7	901.7	34.1	620.8	30.3	41.0	45.3	2967.3	Medium radiation
13	474.7	29.5	495.4	30.3	391.9	27.6	21.1	26.4	3242.4	Medium radiation
14	686.4	32.3	711.0	33.2	500.6	30.3	37.1	42.0	4407.7	High radiation
15	672.4	32.0	708.8	33.4	499.7	29.7	34.6	41.8	3252.4	Medium radiation
16	430.34	30.0	440.4	30.9	358.0	28.1	20.2	23.0	3252.4	Medium radiation
17	106.8	32.1	1082.8	32.7	703.7	30.4	51.2	53. 9	4919.8	High radiation
18	471.4	29.3	488.8	31.2	294.0	26.3	60.4	66.3	2625.3	Medium radiation
19	401.8	30.6	421.5	32.9	313.7	27.2	28.1	34.4	3990.9	Medium radiation
20	611.1	31.3	638.4	32.3	464.1	29.2	31.7	37.6	2040.8	Low radiation
21	407.1	29.9	428.9	30.1	340.0	27.8	19.7	26.2	5374.8	High radiation
22	807.5	32.1	834.2	33.1	568.0	30.1	42.2	46.9	6055.9	High radiation
23	308.3	27.7	328.0	28.6	258.5	25.8	19.3	26.9	4676.4	High radiation
24	352.2	28.7	375.4	29.5	315.5	26.9	11.8	19.0	3120.0	Medium radiation
25	1062.6	33.1	1077.4	34.6	694.0	30.6	53.1	55.3	6055.9	High radiation
26	1100.7	32.8	1116.1	34.5	694.2	30.2	58.6	60.8	5502.0	High radiation
27	1016.1	32.8	1029.9	33.4	695.2	31.2	46.2	48.1	5115.2	High radiation
28	819.3	30.9	849.6	31.6	627.4	29.1	30.6	35.4	4048.6	Medium radiation
29	843.6	32.9	857.4	34.0	532.7	30.8	58.4	61.0	4393.9	High radiation
30	980.0	32.3	999.6	32.9	700.1	30.7	40.0	42.8	5077.2	High radiation
Average	607.2	30.6	628.6	31.8	448.8	28.6	31.9	37.0	-

* Single-axis tracking PV system (astronomical programming). ** Single-axis tracking PV system (LDRs). *** Static PV system.

shows the average performance of the three systems, which have different solar production and percentage of gain on different days with different sky conditions. Figure 16 presents their energy and temperature performance. For calculation purposes, energy (Wh) and gain (%) have been estimated considering the following equations:

$$Energy \left(\mathrm{W}\mathrm{h}\right)=\sum _{i=1}^n{E\left(min\right)}_i$$

(1)

$$Gain\left(\%\right)={\displaystyle \frac{\left[{Energy\left(\mathrm{W}\mathrm{h}\right)}_{Dual-axis PV}\right]\ast 100}{{Energy\left(\mathrm{W}\mathrm{h}\right)}_{Static PV}}}-100$$

(2)

According to these data, single-axis tracking systems exhibit higher energy production compared to static solar systems under similar conditions. The solar tracker system employing astronomical programming and the one utilizing LDR sensors demonstrate energy enhancements of 31.8% and 37.0%, respectively; this confirms the simulated data from the studies presented by [22,23]. The highest values of gain for both systems are achieved on day 26 (sunny), with 58.6% and 61.0%, and the lowest values on day 5 (cloudy), with 18.1% and 21.9%, respectively. The superior performance of the LDR-based solar tracker system in contrast to the astronomical-programming-based counterpart is attributed to the former consistently aligning itself perpendicular to the Sun, while the latter updates its position hourly. Conversely, from an energy point of view, the one-axis solar tracking system based on astronomical programming has greater efficiency due to its simplified design and low energy consumption, which gives it a great advantage for use in equatorial regions.

Table 4. Energy consumption per component.

Name	Consumption (W)	Usage Time (h)	Energy (Wh)
Arduino Mega + RTC	1.2	24	28.8
LDR sensor	0.1	12	1.2
S-A_TPV1: Stepper motor + TB6600 Controller	0.3	12	3.4
S-A_TPV2: Stepper motor + TB6600 Controller	2.4	12	33.2

shows the energy consumption of the electronic components used for each solar tracker system, and

Table 5. Electrical consumption per system.

Name	Gain (%)	Energy Losses (Wh)	Losses (%)	Total Gain (%)
Energy S-A_TPV1	31.9	32.2	6.5	25.4
Energy S-A_TPV2	37.0	63.2	12.8	24.2

shows their total gains and losses.

Based on the presented energy data, the net gain, factoring in losses, exhibits notable similarity between the two systems: 25.4% for the solar tracker system employing astronomical programming and 24.2% for the tracker system relying on LDR sensors (refer to Table 5). It is noteworthy that, in the former system, the stepper motor is activated intermittently, for 2 min per hour, whereas in the latter, the motors require continuous energization for real-time correction of the panel’s position relative to the solar perpendicular.

From both an energy and complexity standpoint, the single-axis solar tracker system utilizing astronomical programming demonstrates advantages suitable for deployment in equatorial regions. Furthermore, there is potential for an increased gain in this system by refining the solar positioning adjustment to a more frequent interval, such as every 15 or 30 min.

4.2. Comparative Analysis: Two-Axis vs. Single-Axis Solar Tracking Systems

This subsection presents a comparative analysis of three single-axis solar tracking systems: one simulated and two developed through our technical research (an LDR-based system and an astronomical-programming-based system). The systems were evaluated in different locations within Ecuador, specifically in Quito and Manta; this analysis highlights the distinctions between the simulated model and the practical implementations, emphasizing the advancements and findings from our research on the two single-axis tracking systems.

Table 6. Comparative analysis.

Aspect	Simulated Single-Axis System [10,21]	Single-Axis System (LDR-Based)	Single-Axis System (Astronomical Programming)
Location	Quito, Ecuador	Manta, Ecuador	Manta, Ecuador
Tracking System Type	Single-axis	Single-axis	Single-axis
Monitoring System	Simulated	Real-time IoT-based	Real-time IoT-based
Tracking Mechanism	Astronomical solar tracking algorithm	Mechanical and electronic closed loop based on LDRs	Mechanical and electronic open loop based on astronomical programming
Energy Efficiency Enhancement	Average: 27.3–30%	Average: 37.0%	Average: 31.9%
Energy Consumption	N/A	63.18 Wh	32.16 Wh
Complexity	Lower	Lower	Lower
Weather Condition Sensitivity	N/A	Stable operation, less affected by weather conditions	Stable operation, less affected by weather conditions
Potential for Further Improvement	N/A	Moderate	Significant
Applicability in Equatorial Regions	Highly suitable, notable efficiency gains	Highly suitable, notable efficiency gains	Highly suitable, notable efficiency gains
Overall Performance	Good	Good, with the potential for further enhancements	Excellent, with potential for further enhancements

summarizes the key aspects and performance metrics of this comparison [18].

The comparative analysis highlights the strengths and limitations of each tracking mechanism. The simulated system serves as a useful benchmark, demonstrating an average energy efficiency enhancement of 27.3% and its potential effectiveness in capturing solar energy. However, as a simulated system, it does not incur any real-time energy consumption and lacks the practical complexities and variabilities faced by operational systems.

In contrast, the practical implementation, both based on LDR sensors and astronomical programming, exhibited proven efficiency gains of around 37% and 31.9%, respectively. The LDR-based system showcased slightly higher energy enhancements, likely due to its continuous real-time adjustments, albeit with increased energy consumption. Conversely, the astronomical-programming-based system offered a simplified design and lower energy consumption, demonstrating significant potential for equatorial regions. These findings underscore the viability of single-axis solar tracking systems, particularly those leveraging astronomical programming, in enhancing energy capture efficiency, thus presenting a promising avenue for sustainable energy solutions in equatorial regions like Manta, Ecuador.

5. Conclusions

This research conducted a comparative analysis involving a static photovoltaic (PV) system and two single-axis PV tracking solar systems, each utilizing distinct methodologies, operating within an equatorial region. An automated monitoring system was utilized to systematically evaluate energy production under standardized conditions. The motivation for this investigation arises from the limited existing studies addressing the utilization of sun-tracking solar systems in proximity to equatorial latitudes. Consequently, it was imperative to undertake further investigations to elucidate the operational dynamics of these solar systems in enclaves near Ecuador.

A real-time IoT monitoring system was effectively deployed to acquire pertinent variables for performance comparison between a static solar system and two single-axis solar tracking systems: a single-axis solar tracking system based on astronomical programming and one based on light-dependent resistors. The outcomes revealed an average gain of 31.88% and 37%, respectively, in comparison to the static solar system. After accounting for losses, the study reveals a marginal difference between them, with the former achieving 25.4% and the latter 24.2%. Notably, the intermittent activation of the stepper motor for two minutes per hour in the astronomical-programming-based system contrasts with the continuous energization requirement for the LDR sensor-based system. Considering both energy efficiency and system complexity, the single-axis solar tracker system with astronomical programming emerges as advantageous for deployment in equatorial regions. This research suggests that adjusting the solar positioning intervals to every 15 or 30 min, instead of every hour, could further increase energy capture. This optimization would allow the system to align more precisely with the Sun’s position, maximizing solar radiation exposure and enhancing system efficiency. The single-axis solar tracking system based on astronomical programming is particularly suitable for equatorial regions. The simplicity and efficiency of this system make it ideal for maximizing solar energy capture in these areas, where solar radiation intensity is high and constant throughout the year. This approach not only improves energy efficiency but also reduces maintenance complexity and costs.