Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends

: Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. The need to optimise energy systems, ensure power continuity, and balance energy supply and demand is driving the continuous development of forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasises the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. The article’s relevance consists of providing up-to-date knowledge on solar forecasting, along with insights on emerging trends, future research directions, and anticipating implications for theory and practice.


Introduction
The active movement towards carbon neutrality and net-zero-emission economies solidifies solar energy's position among renewable energy sources, resulting in a rapid increase in the number and capacity of photovoltaic power plants in many countries [1].The increasing production of solar energy elevates the importance of accurate solar forecasting for ensuring grid stability, economic efficiency, operational planning, market participation, technological advancement, regulatory compliance, and energy storage optimization.Strong volatility and intermittency of solar energy generation require the advancement of adequate forecasting methods concerning meteorological and geographical characteristics of plant location [2,3].Forecasting solar irradiance is essential in planning and operations to deal with energy supply and demand uncertainty, balance and optimise the system, and ensure power continuity [4][5][6][7][8][9].Due to the technological and economic limitations of energy storage solutions, using other, mostly conventional, sources to cover energy shortfalls and, at the same time, utilising solar surpluses for production becomes necessary.Accurate forecasting is crucial at all levels of an energy system, including control, operation, management, the financial viability of energy companies [10], and the trajectories of Energies 2024, 17, 3156 2 of 27 sustainable and responsible innovation [11].Spatial resolution and time horizon determine the application of forecasts.Controlling power distribution, ensuring network stability, and regulating voltage require a time horizon in seconds [5,12].Forecasts from minutes to hours support power reserve management and load optimisation [13], day-ahead forecasts are used for transmission planning and unit commitment [6,8], and a year scale for capacity/network global management [7,14].However, it is important to consider the energy system as a whole, including the various energy system participants, through hierarchical forecasting [15,16].
The rise in solar energy production has resulted in increased demand for enhanced solar energy forecasting methods [17].The penetration of solar energy raises the cost of decisions based on incorrect forecasts at each power system level (transmission, distribution, microgrid, and household), as the costs of forecast errors might reach 75% of the levelized cost of electricity from a typical PV system [18].The importance of solar energy forecasting is reflected in numerous publications.The bibliometric study has revealed over 12,000 works (articles, chapters, etc.) during the period 2013-2023 (database: Scopus; search query: TITLE-ABS-KEY (solar AND forecasting) AND PUBYEAR > 2012 AND PUBYEAR < 2024).A significant increase in the number of articles on solar energy forecasting dates to 2018, with over 1000 per year.There is also a rapid growth in systematic reviews (SR) of previous studies.Such a large number of publications justifies an attempt to analyse and synthesise them collectively.Such a large number of publications justifies an attempt to analyse and synthesise them collectively.The authors aimed to use a meta-review of previous systematic literature reviews to assess the state of the art of solar radiation forecasting methodology.
Meta-review (MR) evaluates and synthesises evidence from existing systematic literature reviews (SLR) [19,20] and, in this way, facilitates broad comparisons [21].It is referred to in the literature as an overview of reviews (OR) [22], meta-meta-analysis, tertiary study, umbrella review, and overviews of systematic reviews.Recently, it has gained increased popularity [21,23], but is still underrated in the field of renewables forecasting.The main advantage of MR is to provide a summary synthesis of the analysed reviews to expand research issues beyond those addressed in the individual reviews and to combine them [23].It is considered particularly useful in areas where many literature reviews have already been published since it allows integration and condenses knowledge [22].
Although the method is not new (e.g., [21,24]), the rapid growth of data and the new advances in search tools and electronic databases have posed new challenges in mapping the state of the art, especially in interdisciplinary topics [25], e.g., engineering management or production management research.The article addresses the problem of determining the meta-review methodology's scope, techniques, and conditions in solar forecasting.To the best of our knowledge, this is the first comprehensive overview of reviews on solar forecasting.The article analysed the scope of the review articles.The research focused on a typology of solar forecasting methods.
This article is organised as follows: in the next section, the concept and methodology of meta-review, along with the approach employed in this article, are presented.Then, a bibliometric and text analysis of reviews on solar radiation forecasting is summarised.Concluding the reviews, a typology of solar forecasting models and methods is discussed.The article ends with a summary and future research directions.

Research Methodology
The literature review serves both as an introduction to research and as a method on its own.It is a key part of every research project or paper since, as referring to current knowledge, it explains the theory behind and meets the paradigm of continuity, accumulation, and development of scientific knowledge [22].It provides evidence for defining the research gap, motivation [26], and opportunities, challenges, and guidelines for future research [27].The methodological discipline, which lies behind SLR, impacts the synthesis and evaluation of materials and information and significantly affects the quality of associ-Energies 2024, 17, 3156 3 of 27 ated further research [28].Rapidly evolving digital tools such as text mining powered by natural language processing enable replicable rapid large-scale analysis and, in some cases, provide a helpful summary; however, they do not replace expert knowledge [29].
An overview of reviews is a type of systematic review of a large but aggregated number of papers to generalise information contained in previous publications or primary sources with clearly structured procedures.Although there are some unique methodological challenges, many methods used to conduct SLR are suitable for overviews of reviews [26].The meta-review procedure is quite similar to formalised systematic reviews, although this method focuses on systematic reviews rather than primary studies [25].
A general framework for SLR and meta-analysis consists of the following steps: (i) defining the objectives and research question(s), (ii) selecting eligibility criteria, (iii) literature search, (iv) data extraction and synthesis, (v) assessing bias risk and quality, (vi) overview and interpretation of results, and (vii) concluding the overview [23,26].The overview framework might be divided into two stages: first-developing and populating, with four steps: (i) specification of the aims and scope, (ii) specification of the eligibility criteria, (iii) selection search methods, (iv) data extraction, and second stage-identification and mapping evaluations that consist of (i) assessing the risk of bias and (ii) certainty of the evidence, (iii) synthesis and summary of the findings, and (iv) interpretation of findings and concluding [20].Overviews could follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, which consists of the subsequent phases: identification, screening, and included [27,30].It obligates to (i) define a clear scope, (ii) do strategic searches, (iii) consider the datedness of the SRL, (iv) address overlap among SLR, (v) apply review quality tools, and (vi) report the meta-review findings.The synthesis of reviews may take the form of narrative, semi-quantitative, or quantitative [31].
The main principle of overviews is the complete and transparent reporting of previous reviews [19].The roles of a meta-review are to identify gaps in the literature, to explore and contrast reviews, and to summarise the evidence from broad comparisons [21].Identifying the inconsistencies between systematic reviews includes, among others, research questions, samples, quality, and selection criteria [21].Summarising and concluding the literature review findings and evidence might benefit new uncovering information [25].
This meta-review aims to examine and collate systematic reviews, summarise the evidence and identify the main themes of the analysis of solar forecasting.The reviews were compared based on input data, methods analysed, classification, and findings.The research process adapted in this work has been illustrated in Figure 1.It consists of translating the aim of the work into search strings and inclusion and exclusion criteria.A broad approach was chosen to ensure no important publication was missed.First, a wide range of keywords was selected, and subsequently, irrelevant terms were eliminated to identify those that could characterise actual and relevant reviews.The original set of solar, radiation, irradiance, and photovoltaic terms was limited to solar.The set covered initially: review, state, recent, advance, trend, development, taxonomy, categorisation, and classification turned out to be sufficient for a review in keywords.The result search query combined the terms (Scopus): TITLE ((forecast* OR predict*) AND solar) AND KEY (review).The literature dataset was also supplemented according to the snowballing procedure.Finally, a retrospective procedure was applied to remove non-relevant publications and discard duplicates.The search was conducted in Elsevier's Scopus, Web of Science Core Collection (WoS) and IEEE Xplore and covered the period until 1.1.2024.Exclusion criteria were papers that were not written in English and conference papers.Upon initial bibliographical analysis, it was discovered that the earliest review-type publications were released at the beginning of the 21st century [27], but the most significant increase has been recorded since 2013.It should be noted that the first works were not a typical review but rather a presentation and discussion of methods with examples [32,33], and the literature review aims to provide background to select methods for testing and comparison [34].Analyses of reviews conducted in recent years are more comprehensive and stick to the methodology, but this is not the rule, especially in the case of conference presentations, e.g., [35][36][37].The actual analysis covers the last 10 years.
Examining the content of the received sets of articles at the preliminary screening at the initial stage of the study, numerous papers have been identified that focus on the evaluation, comparison, and discussion of various methods/models/techniques on the same data [38][39][40][41][42][43].They were excluded from the further analysis.There are also works containing lists of articles on solar forecasting with limited aggregation and summaries [33].Moreover, as mentioned above, articles that aim to improve/develop forecasting methods often include an in-depth literature review [34,44].The state-of-the-art provides the background for the proposed forecast models [45,46].A review of solar techniques might also precede a discussion on power system security, scheduling, and operations [47].
A particular type of review paper focuses on bibliometric analysis.The main advantage of literature reviews using bibliometric analysis and clustering software is the number of references considered.Some works rely on quantitative bibliometrics performed using software such as VOSviewer, which allows for keyword screening [48], or the Google Scholar database and its search engine [49].Text mining undoubtedly has great potential in the literature review.The challenge of automatic review is the proper dictionary construction, selection, and interpretation of terminology and their association to provide in-depth analysis and synthesis with text-mining software [49].
Sometimes, the declared review is not a classical exploration literature review but should rather be labelled as a reverse/confirmation review.This means that defined a priori methods are evaluated with examples of use [32,40,50,51].Such works can be referenced as reviews of techniques described in the literature with a presentation of their advantages and disadvantages [6,52].Some articles consist of general or summarising discussions on selected aspects of solar forecasting in power systems and the penetration of solar power generation, with supporting in-depth reviews and citations [53][54][55].The final list of publications includes synthesising and classifying works in solar forecasting.The next section contains review papers on solar energy forecasting that were selected as the basis for this meta-review.

Bibliometric Analysis of Reviews
The first stage of analysis revealed 36 noteworthy reviews containing an analysis, synthesis, and classification of works on solar forecasting.According to the Scopus database, 28 papers were classified as reviews and 9 as articles.Figure 2 illustrates the distribution of articles by subject area and by year.Table 1 includes the names of the journals that published the reviews.Table 2 consists of the authors' keyword frequency analysis (grammatically adjusted).The results indicate a high interest in AI/ML-powered solar forecasting as well as hybrid models.More nuanced insight into methods discussed in solar forecasting review papers is possible thanks to the method-oriented word cloud, which was developed on the basis of the contents of all relevant papers listed in the references to this work (Figure 3).Energies 2024, 17, 3156 8 of 27

Solar Forecasting Reviews
Table 3 includes the list of reviews.All the analysed papers emphasise that the research on solar forecasting is rapidly expanding.This is related to the increasing penetration of solar PV due to its environmental and economic benefits.The works indicate that energy is the foundation for economic and social growth.Precise forecasting plays a crucial role in the shift towards a more renewable energy profile and in cutting costs in the power system [66,70].The reviews mainly covered the analysis of primary data, sometimes with references to the results of previous reviews, e.g., [2,58,69,71].Classes of SVM for solar: (i) air heater system, (ii) radiation, (iii) collector and photovoltaic systems, (iv) insolation, (v) solar irradiation.
One of the conclusions: SVM modelling is famous for its simplicity, efficiency, and low computational cost.Keywords: support vector machine, solar energy, wind energy, forecasting models.
One of the conclusions is that the classic stratification of solar forecasting approaches has become outdated.The potential research topics have been proposed.Five aspects of solar forecasting were revealed: (1) base forecasting methods, (2) post-processing, (3) irradiance-to-power conversion, (4) verification, and (5) grid-side implications.Keywords: review, solar forecasting, atmospheric sciences, power systems, grid integration, carbon neutrality.
Data: n/a.

Scope of Review Reviews
In principle, all the reviews consider classical error metrics to forecast comparisons.The most commonly used were Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and their derivatives, e.g., normalised RMSE (NRMSE), Median Absolute Percentage Error (MdAPE).However, other metrics were also noted [66], e.g., Kolmogorov-Smirnov Integral [2,9], Nash-Sutcliffe efficiency [2], and others.In general, forecasts should be consistent, high quality, and beneficial to the users, and the adequacy of forecasts cannot be fully described by a single measure of error [15].The accuracy of the forecast translates directly into its value for electricity market participants, although the economic value of solar forecasts is seldom quantified [18].
Among the papers, there are general overviews, but also papers dedicated to methods of one type or even focusing on a homogeneous subclass of models, allowing a deeper look into the structure of the models and collating the results.Particular attention is given to methods that can be categorised as AI [7,33,62,68,70].These include articles comparing various AI models [67] and comparing the AI model with other empirical models [61].AI methods were already well represented in the first comprehensive reviews [32,61].In recent years, the number of articles using various AI techniques to predict solar energy has increased exponentially.This can be related to software development and the ease of using statistical or ML methods [53].Some works focus on methods dedicated to a selected time horizon, e.g., intra-hour [9].Table 5 includes the scope of reviews due to method classification."+" means that a given group of solar forecasting models is included in the indicated review publication.* data-driven; ** including special AI; *** hybrid and ensemble.

Solar Energy Forecasting Methods and Their Classification 4.1. Solar Forecasting Process and Data
Considering solar forecasting, there are three main approaches depending on input data: (i) models that utilise endogenous data (historical series from the PV plant [79]), (ii) models based on exogenous data (sky or satellite images, meteorological characteristics, e.g., as solar irradiance, humidity, wind speed, cloud cover, air temperature), and (iii) mixes that analyse different sets of inputs [66].The popular inputs are (i) historical and current irradiance, (ii) meteorological data, (iii) sky images, and (iv) others [9].Types of sources of data can be sky cameras, sensor networks, and satellites [8].In the case of solar energy forecasting applications, solar radiation is considered the most significant parameter, with a correlation of over 0.98 with PV power output [63].It is the most exploited, both in his first works [32] and now.Among other meteorological data used [75], the sunshine hours and air temperature are found to be adequate inputs [42].The most popular input parameters are temperature, humidity, wind speed, and less frequently: wind direction, precipitation, cloud cover, solar zenith angle, pressure, and others [57].Recently, air pollution has attracted attention [70].
Among the variety of methods, artificial intelligence has gained significant attention due to its high effectiveness and accuracy in forecasting solar energy generation [70,76].AI research in solar forecasting is rapidly growing with expanded applications [7,49].The most common term in articles on solar radiation forecasting is ANN rather than other ML or DL models [7], although this is changing [70].
The AI models on solar irradiance are used in three ways: (i) structural models based on other meteorological and geographical data, (ii) time-series models based only on the historical data on solar irradiance, and (iii) hybrid based on both solar irradiance and other exogenous variables [7].
The advantages of ANN include: (i) less formal statistical training, (ii) detection of complex non-linear relationships between variables, and (iii) multiple training algorithms [52].AI methods outperform traditional methods in many cases [69] due to their excellent performance in the description of non-linear and complex processes [70].However, the comparative advantage of ANN was not always noted.The spatio-temporal vector autoregressive (VAR) model for spatially sparse data may result in a lower forecast error [39].In certain conditions, the ANN and ARIMA methods are equal in terms of the quality of forecasting [7].The significant disadvantages of ANN are: (i) the "black box" nature, which means that the input data and the result are known without information about the process inside, (ii) the need for more computational power, and (iii) the tendency to overfit [52].
The general data mining process for predictive analysis consists of (i) data selection, (i) preprocessing, (iii) transformation, (iv) data mining, (v) interpretation/evaluation, and (iv) knowledge.In the case of ANN prediction tasks in solar energy applications cover: (i) selection of input and output data; (ii) division of the set into training, test, and verification sets; (iii) development of the model; (iv) selection and training parameters, error calculation and verification; (v) selection of the model [42,61].This can be abbreviated to the process of building a machine learning model through (i) data preparation, including the input parameters, (ii) the selection of features, and (iii) the development of the model with evaluation [70,79].It is generally consistent with the process of deploying time-series techniques [80].In the case of physical models, one of the most challenging stages is developing a model to map the relations between input variables and output variables [47].
The role of pre-processing or data feature selection has already been emphasised as a stage that improves the quality of data and thus increases the accuracy of the forecast [5,6,59,63,65,70], even in the first review works [32].Attention is paid to the post-processing phase to model local effects [32,50,65] as a practice to improve the initial forecasts.In the case of ML, post-processing methods might include discriminant analysis and principal component analysis, naive Bayes classification, Bayesian networks, and data mining approaches [7].Other techniques are wavelet transform, Kalman filter, empirical mode decomposition, self-organisation map, normalisation, and trend-free [81].The postprocessing task could be divided into: (i) deterministic-to-deterministic, (ii) probabilistic-todeterministic, (iii) deterministic-to-probabilistic, and (iv) probabilistic-to-probabilistic [65].

Solar Forecasting Models Classifications
Solar forecasting methods do not have a set of consistent classification criteria [58].It is not uncommon for reviews to have overlapping proposals for grouping prognostic approaches, e.g., [46].Details on the classification of solar energy forecast models in the analysed reviews are provided in Table 3.
Traditionally, in the first works and repeated later, forecasting methods are broadly classified into (i) statistical (based on historical time series, e.g., ANN, MPL, SVM, ARIMA, RNN), (ii) physical models (based on atmospheric methodological data, e.g., NWP), and (iii) ensemble approach [34,37,69] or hybrid [82], sometimes with distinction persistence method [8].The following breakdown of forecasting techniques is also proposed: (i) persistence method, (ii) physical techniques (NWP and satellite-based), (iii) linear statistical approaches (e.g., ARMA), (iv) artificial neural networks, and (v) fuzzy logic models [6].Generally, ANN is classified as a statistical method.However, other AI methods, such as ML models, ELM, and SVM, are sometimes clustered in advanced methods [78].Combining statistical and ML models in the data-driven class was also proposed [83].
Another proposition of classification is: (i) the empirical approach based entirely on data, and (ii) the dynamical approach practical for modelling large-scale solar radiation prediction [52].Two basic classes of models can be identified based on the forecast horizon criterion: (i) for short-term forecasts up to 6 h (extrapolation and statistical processes), and (ii) for forecasts up to two days ahead or beyond (NWP models).A further standard division is that between (i) probabilistic (providing confidence intervals, in which values are considered within a certain probability) and (ii) deterministic (single value) [37,64].
Many works emphasise the advantages of hybrid and ensemble approaches in improving forecasting accuracy and providing promising solutions for different forecasting horizons [6,49,54,58,78,81].Ensemble models combine the results of many individual models, while hybrid models combine different techniques or algorithms and take advantage of ensemble techniques, creating sophisticated model structures.
The combining approach could serve as the primary method in a hierarchical multiplestep approach but can also be applied in the pre-processing or post-processing stage [34].However, they must be tuned appropriately [6].Generally, they surpass the best alternative single approach, although this is not always the case [34].Simple techniques might give high accuracy if the input parameters are properly selected, filtered, and pre-processed [12].
In the ensemble approach, there are two methods: (i) "competitive" (parallel) when the final forecast is an average of the individual forecasts, and (ii) "cooperative" (sequential) when the prediction process consists of a sequence of sub-tasks solved individually and the final forecast is a sum of the subtask outputs [34,54,69].Combining, boosting, blending, and slacking methods can be considered in sequential ML.In the case of the parallel, a popular technique is bagging [79].

Forecasting Techniques' Adequacy to Forecast Horizon and Resolution
Many works address the problem of fitting the model to the forecast horizon.Early work indicated that models such as ARIMA are suitable for modelling linear time series, and ANN is preferred for modelling nonlinear time series [84].As the forecasting approaches depend on the available data and also on the required forecasting horizon, many works summarise the existing methods versus time and assess forecasting suitability for forecast horizon and data resolution [2,[6][7][8]32,53,57,69,84].
Table 6 presents the differences in classification in the analysed reviews-a summary of the graphically presented adequacy of forecasting techniques for temporal and spatial resolution, in many cases adapted from previous studies.Hybrid 0.01 km-over 100 km 0 h-over 1000 h [71] Hybrid (data-driven) 0 km-15 km 0 h-over 100 h [9] To generalise, the persistence approach is dedicated to very short-term/intra-hour, statistical for very short, short, and medium-term/intra-hour, and intra-day, and statistical for short, medium, and long-term/intra-day and day-ahead.In detail, persistence is dedicated to seconds, time horizon, and distance up to 10 m, statistical models, e.g., ARMA, ARX, NARX for resolution up to 10 m, methods, e.g., ANN, SVR, for longer distance and temporal resolution from minutes to hours, NWP from hours to days, sky image from 1 m to 2 km and satellite from 1 km to 10 km [6].
Considering only the time horizon, preferred methods for the following ranges: from 1 min to 10 min-persistence of ground measurements, from 10 min to 1 h-groundbased cloud motion vectors (CMVs) data-driven methods, from 1 h to 5 h-satellite-based CMVs and od 5 h to 10 days-NWP models [50].Total sky images are adequate up to 1 km, satellite images up to 100 km, temporal resolution to a few hours (intra-hour, intraday), statistical for maximum intra-day forecasts and 1 km, and physical from 1 h and distance from 1 km [84].The forecast horizon longer than a week ahead with granularity time over 1 h is available only by NWP models [7].However, hybrid models break the stratification.The components might originate from different groups and utilise various data sources in the sequence or parallel approach.The classical taxonomy of solar energy forecasting techniques based on the relationship between space-time resolution needs to be updated.Statistical methods are frequently considered pre-and post-processing tools, not a standalone category, and NWPs with very high resolution can also provide the required results [53].The adequacy of the model to the data also needs to be revised in the case of artificial intelligence, taking into account its dynamic development.

Development of Solar Energy Forecasting Models Classification
The study of review works revealed inconsistencies in the classification, fragmentation, and duplication of proposals.What draws attention are the fuzzy criteria for the models' clustering.
Physical models, also known as "white box" models, are based on a theoretical foundation, fundamental laws, and principles covered in mathematical equations that describe the relationship between the characteristics of a photovoltaic system, solar irradiance, and other environmental and geographical factors that determine the photovoltaic output.These models do not require a large amount of historical data, but still.Their accuracy depends on the availability of weather forecast data [4,79], which must be developed a priori.The most common physical models are numerical weather forecast models (NWP) [84].
It is emphasised that statistical approaches do not require a full understanding and knowledge of the process and rely on mapping the relation between operation data series and NWP data.They assume that future values are determined by past values [4,79].However, forecasting based on a model, e.g., ARIMA, begins with initial data exploration, determining the factors influencing its form, and speculations on components and trends.The model should pass substantive verification and explain the phenomenon under study.
Many times, AI models are categorised as a statistical approach.The AI/ML/DL techniques heavily rely on statistical methods.They have common roots, although considering the dynamic development of AI capabilities, distinctions should be made between auto-regressive models and AI-based models, in which unsupervised learning algorithms decide on the structure and parameters of the models and adapt them to training data.This problem is sometimes avoided by calling both classical statistical models and AI data-driven models [79].
The challenge is to review hybrid models, although attempts have been made (e.g., [54,67,77]).For example, hybrids combine autoregressive and moving average methods with ANN models (SVM, WNN, ANFS, RNN, MLP) with various methods, including ML (the concept of fuzzy sets, SVR, WNN) or NWP and MLP [50].In the general case of having n methods, the number of possible approaches is a sum of combinations with/without repetitions for every possible number of elements from 1 to n. Creativity in creating hybrid and ensemble models is limited by the problem of overfitting, which may occur in redundant analyses.
A summary of the adequacy of forecasting models to the time horizon and data source has been proposed by, among others [58,71].New opportunities are resulting from the development of methods based on artificial intelligence, including image recognition, require revising traditional forecasting methods and their adequacy.Data-driven methods are currently applicable over a wide range of forecast horizons, depending on the forecasting inputs [50].
Table 7 includes a modified version of the proposition based on selected review papers that consider AI models' growing capabilities.It is worth noting that the same lagged or unlagged data can be used in different approaches for model training or direct forecasting.

Conclusions
Creating new knowledge is a complex process that involves recognising the state of the art.The literature review plays a crucial role in various scientific disciplines, both as a research genre and as a methodological one, and it cannot be overstated.This work has compared various review studies on solar forecasting that adopt different perspectives and analyse divergent data to identify recent advancements in the field.Renewable energy, particularly solar, has gained much attention over the past two decades, and the trend continues.
The study has shown that there is no single, accurate, and efficient solar forecasting method for every application.The analysed reviews vary significantly in their approach to the topic, scope, texts included, and conclusions drawn from them.Some are comprehensive, while others are quite limited and selective.However, there are also common elements among them.In solar energy forecasting technologies, there is potential to enhance accuracy, efficiency, effectiveness, and flexibility through novel, combined interpretable AI models, making adaptations through pre-processing and post-processing improvements.
The authors have attempted to synthesise the typology of forecasting methods presented in the reviewed reviews and identify each technique's scope of applicability.Nevertheless, there is still space for further research and an innovative look at the taxonomy of models, their adequacy to the data, and expected results.The advancement of AI unveils fresh opportunities in the real-time prediction of images and data.
This work allows readers to better understand the solar forecasting methods currently in use and their possibilities in real-world applications.Identifying development trends also creates a substantive basis for further conceptual work on elaborating and implementing new robust solar forecasting methods.The authors hope that this work serves the readerswith their specific interest and needs-as an up-to-date companion in navigating the rich and dynamic body of scientific literature on solar forecasting.
This meta-review serves as a comprehensive analysis of the current research and application landscape, which is evolving fast.Considering the dynamic development of this field, there is undoubtedly a need for continuous research and updating of current conclusions.In-depth studies may involve comparisons of selected works from a more homogeneous collection to assess the motivation behind each project and the characteristics and quality of the data used to present the state-of-the-art.Future studies might pay attention to hybrid models, the analysis of their structure validity, and their classification.It is imperative that actions be taken to translate progress in solar energy forecasting into universality and its practical application by network operators at all levels and segments, as it is insufficient [18].It is also worth reviewing solar energy patents and projects implemented by startups.

Figure 1 .
Figure 1.The study flow diagram.Figure 1.The study flow diagram.

Figure 1 .
Figure 1.The study flow diagram.Figure 1.The study flow diagram.

Figure 2 .
Figure 2. (a) Document by subject area according to Scopus; (b) Document by year.

Figure 2 .
Figure 2. (a) Document by subject area according to Scopus; (b) Document by year.

Figure 3 .
Figure 3. Method-oriented word cloud on the basis of solar forecasting review papers (created with WordClouds.com).

Figure 3 .
Figure 3. Method-oriented word cloud on the basis of solar forecasting review papers (created with WordClouds.com).

Table 1 .
Journals published analysed reviews on solar forecasting.

Table 2 .
Keywords' frequency.solar resource estimation, data-driven, weather research and forecasting, deep belief network, weather-dependent renewable energy, wavelet transform, electrical load, solar meteorology, optimization, heuristic algorithm, electricity consumption, input parameters, energy neutral state, carbon neutrality, energy prediction, forecasting horizon, climate condition, solar energy integration, cooperative ensemble forecasting, DCNN, preprocessing, solar variability, evolutionary forecasting methods, spatial, correlation, in situ measurements, regression, temporal resolution, forecast accuracy, time horizon, atmospheric sciences, value of forecasting, SLR, echo state network, smart grid forecasting, wind energy taxonomy, NPW, adaptive duty cycling, NWP 1

Table 3 .
Reviews on solar forecasting (sorted by year).

Table 4 .
The thematic scope of reviews.

Table 5 .
The scope of reviews and classification of solar forecasting models included in the review papers.

Table 6 .
Proposed approaches due to temporal and spatial resolution.

Table 7 .
Adequacy of models to time horizon and data.means that a given group of solar forecasting models is adequate to particular data types and forecasting time horizons.