**1. Introduction**

In the past few decades, the world has experienced considerable growth in environmental awareness, especially regarding climate changes. This rise, allied with an ever-increasing population and limitations to fossil fuels, stimulates the development of Renewable Energy Systems (RES). To reduce greenhouse gas emissions, energy matrices must be composed of more low-carbon sources as opposed to the current fossil-reliant paradigm. Solar photovoltaic (PV) and wind are the future of energy systems if the world is to meet the goals set by the Paris Agreement [1,2].

In addition to the climate-specific Paris Agreement, the 2030 Agenda for Sustainable Development [3] proposes 17 general sustainable development goals with 169 associated global targets. Of these goals, the access to affordable and reliable renewable energy sources is directly aligned with three goals: 7—to ensure access to affordable, reliable, sustainable and modern energy for all; 9—to build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; and 12—to ensure sustainable consumption and production patterns.

However, each of the mentioned energy sources has its own limitations, such as geographical location and unreliability, mostly regarding weather. In the case of solar energy, particularly PV energy conversion to produce electricity, it possesses high variability from various sources (e.g., weather, Earth's rotation and translation movements).

Solar energy's inherent intermittency creates several economical, technical and political barriers against larger penetration [4–6]. Most of the variability components are

**Citation:** Bassous, G.F.; Calili, R.F.; Barbosa, C.H. Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting. *Energies* **2021**, *14*, 6075. https://doi.org/10.3390/ en14196075

Academic Editors: Marcin Kami ´nski and Angel A. Juan

Received: 19 August 2021 Accepted: 18 September 2021 Published: 24 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

deterministic in nature, meaning that they can be easily forecasted and addressed, provided that it is technically possible to do so.

One of the most detrimental variability components is the presence of clouds, which filter the solar radiation and decrease the amount of energy available for photovoltaic conversion. Particularly on days with partial cloudiness and fast-moving clouds, the insolation variation in one solar plant output can reach well over 50% in one minute [7,8]. These fast variations in such a short time may cause technical problems in plant and grid operation, such as voltage variations and current harmonics [5,6,9–11]. To address these variations, it is necessary to forecast them. In the work reported in Reference [8], the need for power system operators to be able to address generation and load profiles over short time-scales was stressed due to the stochastic variations caused by fast cloud transients. Numerous methods for short-term insolation or power forecasting exist; however, for plant and grid operation, conventional statistic forecasting methods based on time series are not well suited [12]. The most widely used physical methods for short-term predictions are sky-image based [13].

Tropical countries, in general, boast higher solar photovoltaic potential in comparison with temperate regions, and, in contrast, these countries also possess lower development indexes. That leads to increased difficulty in acquiring specific equipment for conducting research in solar modeling and forecasting, even more so if such equipment is necessary for implementing mitigation strategies. Figure 1 shows the discrepancy in solar resource availability and, in contrast, its utilization. The red scale represents daily average global tilted irradiance (GTI) [14], and the yellow sun symbols represent the installed capacity normalized by a country's area. In some areas, such as Europe and Central America, only the most important solar generators were kept on the map for clearer data representation.

**Figure 1.** Daily GTI and installed capacity per country [15,16].

The first discrepancy between solar potential and utilization is clearly between African and European countries. Despite having two to three times more average daily global tilted irradiance, most African countries have a couple orders of magnitude less PV installed capacity. Another interesting comparison can be made between Mexico and the United States, because, despite being in the same continent, both have very different development levels, and that is more correlated to the installed capacity than solar resource availability. A similar comparison can be made between Brazil and Uruguay, Morocco and South Africa, and Spain and the United Kingdom. This makes clear the necessity for lower cost equipment, because, by reducing cost barriers, these countries can look to solar energy infrastructure to support their industrial development. Addressing these discrepancies has

been recognized as an important step in achieving the sustainable development goals set for 2030 [3,17].

Aside from the solar resource availability, forecasting is essential to energy generation and distribution. As mentioned in Reference [8], system operators need better information about the stochastic behavior of cloud-induced variability, to increase reliability. Several time horizons and resolutions are necessary to meet the demands of each specific aspect in PV energy management. The focus of this work is on very short-term forecasting to bolster PV plant operation capabilities, reliability, grid integration and grid operation in a scenario of high penetration.

In Reference [12], different irradiance forecasting methods are explored with the objective of proposing a small-scale insular grid forecasting system. Small isolated grids have less system inertia, therefore are more susceptible to the negative effects of RES, especially those caused by PV systems. Each different model available has its advantages and disadvantages and, for a holistic forecasting system, different models should be used in parallel.

Persistence and image-based models fit well, for short-term forecasts, in terms of horizon, frequency and spatial resolution. Other statistical models, as named in Reference [12], also encompass various regression models and learning algorithms, such as artificial neural networks (ANN).

In recent years there has been a rise in research work on sky-image based PV or insolation forecasting [18,19]. Sky-image models keep improving the reliability of very short-term forecasting, as shown in Reference [13]. This tendency points towards the superiority of using sky-images over what Diagne et al. [12] refer to as statistical models. In the study conducted by Kow et al. [20] it becomes apparent just how powerful sky-image based forecasting can be, achieving a detection rate of over 90% of power fluctuation events and mitigation of almost 80% of power fluctuation events with minimal energy loss.

While being a powerful tool, forecasting alone cannot solve the issues caused by high-frequency variability. However, coupled with other systems, such as energy storage systems and power electronics, especially in progressively smarter grids, forecasting can be a valuable aid in increasing PV penetration [9–11,21–24]. The results presented by Kow et al. [20] depict the beneficial effect that short-term forecasting can have on the operation of PV plants.

As some authors have shown, even lower-cost equipment can yield trustworthy results when comprehensively developed and tested [25]. This serves as encouragement for research institutions in developing and less-developed countries to work on their own equipment to provide their scientific and industrial needs.

Looking at the case for Brazil, which meets the criteria for solar resource abundance and developing economy, increasing accessibility to research equipment aligns with the country's goals set for the UN sustainable development goals prioritized for its 2030 agenda. Oliveira et al. [26] point out that Brazilian relay targets, highly influential as well as dependent within the agenda, can be directly impacted by increased affordability in solar power research. Goals such as resource efficiency, upgraded infrastructure, education and institutional capacity on climate change, and renewable energy depend on other goals, but also impact several others.

Reduction of costs associated with determinant goals such as research and development, innovation and economic growth have a high potential of impacting the relay goals previously mentioned [26]. More specifically, the affordability of newer renewable energy technology and their development align with the following targets: 7.1—"ensure universal access to affordable, reliable and modern energy services"; 7.2—"increase substantially the share of renewable energy in the global energy mix"; 7.3—"double the global rate of improvement in energy efficiency"; 7.b—"expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries, in particular least developed countries, small islands developing States and landlocked developing countries [ ... ]"; 9.1—"develop quality, reliable, sustainable and resilient

infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all"; 9.2—"promote inclusive and sustainable industrialization [ ... ]"; 9.5—enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries in particular developing countries [ ... ]"; 12.2—"achieve the sustainable management and efficient use of natural resources"; and 12.a—"support developing countries to strengthen their scientific and technological capacity to move towards more sustainable patterns of consumption and production".

With these possible impacts in mind, the objective of this work is to present and validate a low-cost system for monitoring and modeling short-term variability developed during a Master's course [27].

#### **2. Short-Term Forecasting**

As stated in the previous section, accurate very short-term forecasting is the first step in adding reliability to PV plant operation. The first step in forecasting is to build a model that describes the behavior of the studied phenomenon. To that end, many different models can describe or learn the behavior of PV conversion, some more accurately than others. Table 1 presents the terminology regarding forecasting horizons and their applications, based on the concepts used in References [12,28].


**Table 1.** Forecast horizon categories, granularity and applications.

Within the statistical category mentioned in Reference [12], persistence models are the best fit for the spatial and temporal requirements of very short-term forecasting for a single PV plant. However, it is a naïve predictor, serving as a baseline for more complex models. It assumes the predicted value *X*ˆ*t*+<sup>1</sup> to be best described by its value at a previous time *Xt*. In this case, the modeling and prediction are one and the same; it does not take into consideration the several variables that affect the behavior of real-world PV panels, and that is why it is considered a trivial predictor.

Still, within linear models, the regression models addressed by Diagne et al. [12] use historical data either from irradiance or clear-sky index to make predictions. While better than the previous, naïve predictors in terms of fidelity to the real world, it is still unable to provide forecasts in the required time horizon and resolution. These models, however, fare well from 15 min to hourly forecasts [29]. In the 5 min resolution, results were mixed among the models tested by Reikard [29], but the autoregressive integrated moving average (ARIMA) model started to be outperformed, especially by neural networks. The author also pointed that the ARIMA model exhibited large errors at intermittent intervals, corresponding to the fast cloud transients that deeply impact PV reliability. These intermittent large errors are the events successfully predicted in the work by Kow et al. [20].

Switching over to the non-linear models addressed by Diagne et al. [12], neuralnetwork models attempt to simulate the computational and learning process of the human brain [30]. The complexity, nonlinearity and parallel computational power excel in pattern recognition and perception. The networks are composed of simple processing units commonly referred to as neurons. The network can acquire "knowledge" through a learning process that acts in the interconnection of the neurons, just as synapses would in a biological brain [30].

Neural networks, in their many architectures and sizes, are able to learn from data, in both supervised and non-supervised processes, and apply this knowledge to new data [30]. They are well suited to model complex problems, especially when involving complex relationships between the variables [30], such as forecasting energy conversion dependent on cloud passage, location, time and meteorological variables [31,32].

As mentioned previously, neural networks start faring better against other forecast methods at higher temporal resolution [29]; however, by looking at other studies into the subject, there appears to be a time-resolution limitation in these machine-learning methods for short-term forecasts. Even in the most recent state of the art works with intra-hour forecasting, using time series prediction of irradiance or other atmospheric parameters, the minimum resolution is still 5 min [33,34], which still falls short of the necessary frequency to properly characterize the local solar variability [35]. Still, within the 5 min time horizon, sky images can be used to boost forecasting accuracy when coupled with machine learning models and historic irradiance or power data [36].

The conclusion that can be drawn from the consistent number of time-series models limited to the 5-min time horizon is that the fault is in the type of data used to characterize the relationships involved in the high variability of solar irradiance. As explained before, these models aim to predict the future state of a certain aspect of solar variability. The approaches using cloud tracking in sky images, as proposed by Chow et al. [37] and Kow et al. [20], add components of physical and geometrical modeling of cloud systems. Since the main actor in short-term variability is related to passing clouds, relevant information on their dynamic provides a more comprehensive characterization [38].

The trend in researching sky-based approaches to very short-term solar forecasting began with the work by Chow et al. [37], despite not being the first to approach the subject [38]. The goal behind it is to use physical information from cloud systems, extracted from sky images captured by hemispheric cameras.

Initially, researchers used already existing sky imagers developed for meteorological purposes other than estimating solar quantities [38]. In more recent years, other lower-cost alternatives have been developed for the specific purpose of estimating solar quantities [25,38]. These newer, specific systems are fully programmable and expandable, leaving room for development and expansion, as well as being suitable for use with a plethora of different forecasting models [25,39].

Amongst the already mentioned advantages, specifically designed systems have proven to yield superior results to other non-specific sky imaging systems [25,40,41]. Most likely this superior performance is due to the higher data-acquisition frequency which provides better insight into local short-term solar variability [35]. Another significant difference is that these specific devices do not have a shadow band to occlude the solar disk and part of the circumsolar region. This fact positively impacts the amount of information available for intra-minute forecasts.

Throughout the research process that laid the theoretical foundations of this work, several key works stood out and greatly influenced the work developed here. Table 2 contains these important works in chronological order with their objectives, whether it is forecasting or modeling, and the materials and methods used in the pursuit of these objectives.


**Table 2.** Important works that shaped this research.

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**Table 2.** *Cont.*

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**Table 2.** *Cont.*

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**Table 2.** *Cont.*

As seen in Reference [35], data resolutions of 30 s or less are essential for representing local solar variability. Moreover, most of the works presented in Table 2 do not meet this time resolution constraint, and those that do, possess higher cost systems often with multiple cameras and other sensors. As stated in Section 1, this limits the conduction of higher-resolution studies in less-developed countries, due to this fact, the goals of this work were to try and achieve high-resolution data acquisition and modeling, using low-cost equipment.
