Systematic literature review of photovoltaic output power forecasting

IET Renewable Power GenerationVolume 14, Issue 19 p. 3961-3973 Review ArticleFree Access Systematic literature review of photovoltaic output power forecasting Kıvanç Başaran, Department of Energy Systems Engineering, Manisa Celal Bayar University, Manisa, TurkeySearch for more papers by this authorFatma Bozyiğit, Corresponding Author fatma.bozyigit@bakircay.edu.tr Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this authorPierluigi Siano, orcid.org/0000-0002-0975-0241 Department of Management & Innovation Systems, University of Salerno, Salerno, ItalySearch for more papers by this authorPelin Yıldırım Taşer, Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this authorDeniz Kılınç, Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this author Kıvanç Başaran, Department of Energy Systems Engineering, Manisa Celal Bayar University, Manisa, TurkeySearch for more papers by this authorFatma Bozyiğit, Corresponding Author fatma.bozyigit@bakircay.edu.tr Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this authorPierluigi Siano, orcid.org/0000-0002-0975-0241 Department of Management & Innovation Systems, University of Salerno, Salerno, ItalySearch for more papers by this authorPelin Yıldırım Taşer, Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this authorDeniz Kılınç, Department of Computer Engineering, İzmir Bakırçay University, İzmir, TurkeySearch for more papers by this author First published: 26 January 2021 https://doi.org/10.1049/iet-rpg.2020.0351AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works. 1 Introduction Solar energy is obtained from sunlight that passes through the atmosphere to be used for different processes, such as water heating systems or producing electricity, in addition to the initiation of chemical reactions of natural processes like photosynthesis [1]. This energy is free, inexhaustible, and non-polluting, unlike fossil fuels. As the sun is the world's largest energy source, solar power generated from photovoltaic (PV) systems are the world's second-largest source of electricity followed by onshore wind and hydropower [2]. In the last few years, there has been a great increase in the installation of PVs which convert solar irradiation directly into electricity. According to the International Energy Agency (IEA) market analysis and forecast from 2019 to 2024 on renewable energy and technologies, renewable power capacity is set to expand by 50% (1200 GW, which is equivalent to the total installed power capacity of the United States today) [3]. Despite the fact that PV systems make many contributions to the global economy, large-scale integration of solar PVs will put the reliability and fluctuations of solar power at risk, resulting in an imbalance between electricity production and demand [4]. Therefore, forecasting of PV output power has boosted the development of many studies worldwide to ensure the efficient and economic use of this source. Energy traders can benefit from these forecasts on the energy markets since they can manage the distribution of power while considering growing demands. Furthermore, the increasing development of grid and smart homes has led to the need for knowing the power generated by PV systems in order to set energy flows for further time steps [5]. Accurate forecasting of PV output power can help in planning and scheduling of power dispatch, improving system reliability and power quality, and reducing the impact of uncertainty of PV power generation. There are many research works that forecast PV output power for various time resolutions. The approaches performed in these researches are basically divided into three categories: persistence, statistical, and machine learning (ML) model. Persistence forecasting simply assumes that current PV power generation levels do not change in similar days. In statistical forecasting, various methods ranging from complex computer codes to empirical correlations with varying accuracy can be performed [6]. However, non-linear PV power production due to variable climatic conditions limits the use of persistence and statistical models. Recently, it is seen that ML approaches with their ability to predict complex and uncertain models have become popular for modelling, forecasting, and optimizing non-linear systems. In the current studies, there are two main approaches which are widely used in the forecasting of PV system production: indirect and direct. Direct forecasts calculate the power output of the PV system with respect to historical data while indirect forecasts at first predict the solar irradiation and then the power produced [7]. Both forecasts are approached via similar techniques which are stated in the previous paragraph (persistence, statistical, and ML). This systematic literature review (SLR) is based on those research works, which directly/indirectly forecast PV output power using ML approaches, in order to establish a boundary in the scope. The main objective of this systematic review is to develop a conceptual framework to synthesise and compare the related works proposing approaches to forecast the PV output power. Considering the evaluation of current approaches, we identify the concerns in this domain. The structure of this paper is organised as follows: Section 2 gives information about the aim of the study and research procedures used in the SLR. Section 3 presents the evaluation results of the reviewed studies with respect to research questions. Section 4 concludes the paper and includes suggestions for further studies. 2 Research methodology A review paper is a survey of existing literature on a topic that is used to explain the current state of the topic. Review papers are examined under two categories: narrative reviews and SLR conducted by Kitchenham et al. [8]. Narrative reviews evaluate the studies related to the research topic in a wide spectrum. In the SLR, a detailed and comprehensive survey on particular research questions and subject area is performed in a more detailed and systematic way. Therefore, we developed a research methodology in view of the SLR model in this paper. The general architecture and functional blocks of our proposed SLR model are shown in Fig. 1. First, three research questions (will be explained in Section 2.1) are determined in order to build the framework of SLR. Second, we develop a search strategy that includes paper selection procedures (determining search terms based on research questions and creating search queries). Then, the results of the search process are filtered based on the inclusion and exclusion criteria. Finally, quality assessment criteria are determined and then studies which do not match these criteria are eliminated. Fig. 1Open in figure viewerPowerPoint Illustration of research methodology 2.1 Research questions Specification of the research questions is the most important phase in the SLR methodology since research questions identify the scope and the objective of a review study. In our study, the research questions are determined in order to analyse and evaluate different approaches used in PV power forecasting studies. These questions are listed as follows: RQ1 makes classification according to the materials of PV cells such as mono-crystalline silicon, poly-crystalline silicon, and thin-film silicon (amorphous silicon). We also categorise the studies as standalone systems and grid-connected systems in the scope of this question. Finally, we examine the studies considering the installation of the PV systems (ground mounted or rooftop) and investigate the location where the PV system is installed. RQ2 investigates the data sets used in the reviewed studies. For example, it deals with what kind of data variables (solar irradiance, numerical weather prediction (NWP), cloud cover, wind speed etc.) are included into data set? RQ3 aims to identify the forecasting approaches of the studies in the SLR. According to the answers to this question, the methods that are proposed in the reviewed studies are observed. Since statistical methods are inadequate to determine complex relationships between the features of samples, we concentrate on the studies using ML approaches. 2.2 Search strategy In this paper, we select a well-known digital database, Scopus, to find high-quality papers. The search process is completed on the journal papers and conference proceedings from 2010 to 2020. To find the full text of these studies, determined queries are entered into a selected digital database. The inclusion/exclusion and quality assessment criteria are applied to the search results, and finally, the papers to be evaluated under the SLR are filtered. 2.2.1 Search queries In the search process step, the keywords related to the research topic area are determined. To create search queries, we specify and categorise the search terms based on the research questions using the population, intervention, comparison, outcome and context (PICOC) proposed by Brereton et al. [9]. Population is the criteria that specify the domain of forecasting the PV output power such as ‘PV output power forecast’, ‘forecasting of PV power’, ‘PV output power prediction’, ‘PV output power management’ etc. Intervention consists of the keywords that indicate approaches used for forecasting PV output power such as ‘support vector machine (SVM)’, ‘support vector regression (SVR)’, ‘neural network algorithms’, ‘autoregressive integrated moving average (ARIMA) methods’ etc. Comparison is used to compare interventions of the studies forecasting PV output power. Outcome consists of the keywords about the generated product of the reviewed models such as ‘PV output power forecast’. Context consists of the keywords which are the extended view of the population including whether it is conducted in a smart home or an industry. We do not use output criteria to formulate search strings since all the reviewed works predict the PV output power. The search strings are derived from specified search terms including population, intervention, comparison, and context as shown in Table 1. Table 1. Created search queries using search terms (standalone OR ‘off grid’ OR ‘grid connected’ OR ‘on grid’ OR ‘grid tie’) OR (photovoltaic OR PV OR ‘solar PV’) AND (output OR power OR energy OR ‘output power’) AND (forecast OR prediction OR estimation OR management) OR (‘machine learning’ OR regression OR ‘artificial neural network’ OR ‘statistical approach’ OR ‘fuzzy’) (forecasting OR prediction OR estimation OR management) AND (standalone OR ‘off grid’ OR ‘grid connected’ OR ‘on grid’ OR ‘grid tie’) AND (photovoltaic OR PV OR ‘solar PV’) AND (output OR power OR energy OR ‘output power’) OR (‘machine learning’ OR regression OR ‘artificial neural network’ OR ‘statistical approach’ ‘fuzz — 2.3 Study selection procedure A total of 98 papers are determined as input for the selection process by using our search queries. Appropriate studies are selected using the defined criteria for inclusion and exclusion. With respect to this, first, 93 studies providing inclusion criteria are selected. Then, 74 of the selected studies that include at least one of the exclusion criteria are eliminated. Finally, 62 studies which are relevant to our research scope are identified. Inclusion criteria are listed as: Studies in journals and conferences in the field of energy and information systems are selected. Journal articles and conference proceedings are filtered with respect to publication dates (between 2010 and 2019). Exclusion criteria are listed as: Presentations, workshops papers, informal papers, and tools not based on scientific study are eliminated. Duplicate papers of the same study are ignored. Paper that does not mention ‘forecasting/predicting/estimating of PV output power’ in title and abstract are not included in SLR. 2.4 Conducting the search The quality assessment process is used for the interpretation of findings and determining the power of detailed investigations [7]. After the study selection process is carried out, the quality of each selected study is evaluated according to the four questions: are the context of the study clearly presented? Are the objectives of the study reasonably described? Are the applied methods detailed explained? Is the output of study visualised by a concept identification model? All researchers in this SLR label the primary studies as ‘include', ‘exclude', or ‘uncertain' tags for each criterion. It is decided whether to include a study into the review or not by considering the number of tags. If the number of ‘include’ tags is greater than others, the study is determined to evaluate in the SLR scope. Initially, 87 papers are extracted regarding the results of search queries on digital databases and academic search engine. Then, filtering is performed based on inclusion and exclusion criteria and so 62 papers are selected in this step. At the last step, it is determined under specified quality assessment criteria whether the selected studies are appropriate to be evaluated in this work. Finally, 49 papers are selected to be evaluated in this study (see Table 2). Table 2. Number of studies after implementation of quality assessment criteria Total number of papers Number of papers Article Conference paper 49 42 7 3 Analysis and results 3.1 RQ1: What kind of solar panels are used in the evaluated PV systems? There are three types of technologies used in the solar panels that are commercially available nowadays: monocrystalline, polycrystalline, and thin film (using amorphous silicon). Monocrystalline is usually obtained from a single continuous crystal structure through Czochralski method, which utilises the crucible drawing process [10]. Monocrystalline technology is the most efficient and most spectrum specific of the PV cells. An alternative way of making silicon PV cells is manufacturing using polycrystalline silicon. The ingot (a huge crystal of silicon) is separated into thin layers that are used to make the solar cells like producing monocrystalline cells [11]. The polycrystalline silicon is less expensive than the monocrystalline silicon but is also less efficient. Thin-film cells are constructed by depositing extremely thin layers of PV materials onto a superstrate module backside. Thin film technology is more economical compared to crystalline silicon. However, the efficiency of thin-film technology is lower than crystalline silicon technology since a larger area is required to produce the expected power. Thus, crystalline materials have extensive market share in the solar industry, with thin-film modules estimating for 5% of solar installations across the globe [12]. In this SLR, we categorise the studies according to the material of the PV cells in the scope of RQ1. Tables 3 and 4 give information about the type of PV system, maximum power in the PV module, the material of the PV cell, and placement of the PV system in the reviewed studies. It is observed that most of the reviewed studies make analysis on polycrystalline PV panel technologies since they have a good price-performance ratio and operate under a broader spectrum of light. We also categorise the studies according to installation types (such as standalone systems or grid connected, rooftop or ground-mounted systems). Table 3. Properties of the PV cells, type of system, installed power, and placement used in the PV system Study Year of the study Type of PV system Max power of the PV system Material of PV cell Country PV system is installed PV system placement [4] 2014 grid connected unknown unknown Météo France rooftop [13] 2011 grid connected 18 kWp PV in which each module has power of 170 Wp unknown Chine rooftop of the Renewable Energy Research Centre of Huazhong University [14] 2012 indirect indirect indirect South Korea solar irradiation multiple sites of South Korea [15] 2013 indirect indirect indirect Singapore solar irradiation [16] 2013 indirect indirect indirect USA solar irradiation [17] 2013 grid connected Plant A, Plant B, Plant C, and Plant D PV systems which maximum power in 0.15, 1.0, 0.15, and 5.0 MWp, respectively. unknown Greece Ground mounted [18] 2013 indirect indirect indirect Medina, Saudi Arabia solar irradiation [19] 2013 grid connected 20 kWp plant system which has 174 panel polycrystalline Italy rooftop of the municipality of Trieste [20] 2014 indirect indirect indirect France solar irradiation [21] 2014 indirect indirect indirect Sepang, Malaysia solar irradiation [22] 2014 grid connected 15 kWp unknown Ashland, Oregeon, USA unknown [23] 2015 grid connected 112 kWp thin film Angeli di Rosora, Italy rooftop of a Locciıni group building [24] 2015 grid connected 49.2 kWp PV system which includes 240 panels (Site 1) polycrystalline USA (site 1), Denmark (site 2), Italy (site 3) unknown [25] 2015 grid connected 5 MWp plant system unknown Hohhot, Inner Mongolia, China ground mounted [26] 2016 unknown 128 kWp unknown England rooftop of the Watson School of Engineering, Birmingtom University [27] 2016 grid connected PV system which includes 42 panels unknown Japan rooftop [28] 2016 grid connected PV system which has 921 panel unknown Germany unknown [29] 2017 unknown 1.86 MWp unknown Spain unknown [30] 2017 grid connected 17.16 kWp PV system which has 78 panels unknown Van, Turkey unknown [31] 2017 grid connected 4.99 MWp PV system which each module in has power of 5.21 kWp unknown Lombardy (SL), Calabria (SC) and Sicily (SS), Italy rooftop installation (SL and SS) and ground-mounted (SS) [32] 2017 grid connected unknown unknown San Diego, USA PV system installed at the University of California [33] 2017 grid connected 22 kWp PV system which each module in has power of 235 Wp polycrystalline Tiruchirappalli, Tamil Nadu, India flat roof area of 150 sq. m installed in a reputed manufacturing industry [34] 2017 grid connected Plant 1, Plant 2, and Plant 3 PV systems which each module in 2.0, 1.875, and 2.7 kWp respectively. Plant 1 (polycrystalline), Plant 2 (monocrystalline), and Plant 3 (thin film) Malaysia rooftop of PEARL laboratory in University of Malaysia [35] 2017 grid connected 440 kWp PV system which each module in has power of 5 kWp unknown Ashland, Oregeon rooftop of the Ashland Court House [36] 2018 grid connected 50 kWp PV system which each module (200 panels) in has power of 250 Wp unknown Cardiff, UK rooftop of an educational building Table 4. Properties of the PV cells, type of system, installed power, and placement used in the PV system (Table 3 continued). Study Year of the study Type of PV system Max power of the PV system Material of PV cell Country PV system is installed PV system placement [37] 2018 grid connected 264 kWp Unknown Amman, Jordan rooftop of the Applied Science Private University in Amman [38] 2018 grid connected 2.1 kWp PV system which each module in has power of 250 Wp polycrystalline Poznan, Poland rooftop of the Faculty of Electrical Engineering Poznan University [39] 2018 grid connected 2.448 kWp unknown Seoul, Korea rooftop of a commercial building [40] 2018 unknown 1.356 MWp polycrystalline Cyprus ground-mounted (University of Cyprus) [41] 2019 indirect indirect indirect Austin, TX, USA solar irradiation [42] 2015 standalone standalone PV system which each module in (16 panels) has power of 250 Wp monocrystalline Newquay, Cornwall, UK rooftop of a building located in the town of Newquay, Cornwall [43] 2016 unknown unknown unknown China Ground mounted (three adjacent solar farms) [44] 2019 unknown unknown unknown Gumi, South Korea PV operator located in Gumi [45] 2019 unknown unknown unknown Australia Desert Knowledge Australia Solar Centre [46] 2019 unknown 23.4 kWp PV system in which each module (120 panels) in has power of 195 Wp monocrystalline Australia ground mounted (1B DKASC PV power station in Australia) [47] 2020 grid connected unknown monocrystalline Australia ground mounted (DKASC PV power station in Australia) [48] 2018 grid connected unknown unknown unknown unknown [49] 2019 unknown unknown unknown Australia three solar power plants in Australia [50] 2010 grid connected 20 kWp PV system which each module in has 120 Wp unknown Tokyo, Japan PV generator installed at the Engineering Campus of the Tokyo University [51] 2015 grid connected PV systems having power ranges from 2.64 MWp to 958 kWp unknown Spain five PV plants situated in northern Spain [52] 2019 grid connected PV systems having power ranges 10 kW to 15 kW unknown Zhejiang Province, China unknown [53] 2015 grid connected 2245 Wp.PV system 10 monocrystalline 11 molycrystalline Milano, Italy rooftop installed at SolarTechLab at Politecnico di Milano [54] 2012 unknown 750 Wp PV system monocrystalline Istanbul, Turkey constructed in the meteorological park of Istanbul Technical University, [55] 2013 unknown unknown unknown Okinawa, Japan, Unknown [56] 2020 grid connected San Juan and Four Corners plants with capacities of 1.800 MWp and 2.040 MWp, unknown Nevada, USA two PV plants situated in Desert Rock station in Nevada [57] 2020 grid connected 30 kWp PV system unknown Illinois, USA rooftop installed at University of Illinois in Urbana-Champaign [58] 2019 unknown unknown polycrystalline Qatar rooftop installed at Qatar University [59] 2017 grid connected 27.34-kW unknown Korea PV system installed in Gumi Electronics and Information Technology Research Institute (GERI) [60] 2019 grid connected 12.6 MWp solar farm unknown Florida, USA ground mounted By considering the RQ1, we also analyse a study that compares different physical models (PV cell type) to predict PV performances [53]. The proposed approach is applied to both monocrystalline and polycrystalline PV panels installed at the SolarTechLab, Politecnico di Milano. Experimental results show that the thermal model, the type of silicon cell, and the data set used for its calibration are the most necessary factors affecting PV power forecasting. It is determined that forecasted power output is more accurate by using experimental data and Sandia's thermal model in monocrystalline cell type. 3.2 RQ2: What are the details of the used data set in the reviewed studies? Data set is the most important parameter to test the performance of the proposed model in scientific studies [61]. In this question, we review the properties of input data which feeds proposed ML approaches and generated output of the current studies. We classify the papers considering forecasting horizon, data resolution in time series, and period of historical measurements (meteorological factors, solar irradiance, PV output power etc.). First, we investigate the features used in the ML methods. The widely used features in the current studies are previous power measurements, weather temperature, humidity, solar irradiance, daytime duration, and cloud coverage. Some studies implement feature selection process to determine the factor which mostly affects the forecasting accuracy. The analysis results of the studies, which utilise the feature selection process indicate that irradiance, temperature, and historical power measurements are the most prominent factors to predict the power. After reviewing the input parameters in the current studies, we inspect the time resolution of historical measurements and forecasting horizons which vary according to the economic purpose of PV system installations. We classify the forecasting horizons into four time periods: very short term (1 min–1 h), short term (intra hour to one day), mid-term (1 month–1 year), and long term (1–10 y). Midterm and long-term solar forecasts are useful for energy resource planning and scheduling. In comparison, short-term solar forecasts help load tracking and pre-scheduling, which reduce the need for frequency control in the actual environment. Moreover, excessive or low PV power output will affect the grid's safe and reliable operation, which limits the application of large-scale PV systems connected to the grid. Therefore, it is necessary to establish a well-designed very short-term model to predict the PV modules' output power in time accurately. Tables 5, 6 and 7, which give information about the details of the data sets used in the reviewed studies. Table 5. Information about the data set used in the reviewed works Study Year Features in the data set Data resolution Data period Selected features Forecasting horizon Output [4] 2014 solar irradiation, sky global irradiation, cloud cover in the low levels of the atmosphere, total irradiation, relative humidity, total cloud cover, air temperature, humidity, wind speed, wind direction, dew point temperature 10 min from 2009 to 2011 solar irradiation, cloud cover, air temperature, wind speed and direction short term (hourly) PV power [13] 2011 daily solar irradiation, lowest air temperature, highest air temperature, daily relative humidity, daily wind speed, wind direction hourly and daily from 1 January to 31 December 2017 solar irradiation, wind speed, air temperature short term (day-ahead forecast) PV power [14] 2012 satellite images (atmospheric motion vector, clouds, and irradiation) 15 min 4 years’ historical satellite images unknown short term (15–300 min forecast) solar irradiation [15] 2013 sky condition, air temperature hourly (from 08:00 to 18:00) unknown air temperature short term (hourly and day-ahead forecast) solar irradiation [16] 2013 unknown 5 to 60 min from January to March 2011 unknown unknown solar irradiation [17] 2013 NWP (not declared in a detailed manner), solar irradiation hourly and daily from 1 January 2011 to 31 December 2012 unknown short term (day-ahead forecast) PV power [18] 2013 solar irradiation, air temperature, wind speed, wind direction, humidity, atmospheric pressure. hourly From 1 January to 31 December 2011 unknown short term (day ahead) solar irradiation [19] 2013 daily solar irradiation, air temperature hourly and daily from 28 January to 25 May 2009 unknown short term (day-ahead forecast) PV power [20] 2014 daily weather forecasts 5 min and daily June 2008 to May 2009 GHI with ambient temperatures forecasts short term (day-ahead forecast) solar irradiation and PV power [21] 2014 solar irradiation hourly from August to November 2015 unknown short term (hourly) solar irradiation [22] 2014 solar irradiation, environmental temperature hourly unknown unknown short term (1 h) PV power [23] 2015 historical data of solar irradiation, environmental temperature, output power 15 min unknown solar irradiation, the temperature short term (15 min and day ahead forecast) PV power [24] 2015 cell temperature, wind speed 15 min from 1 July 2010 to 31 December 2011 unknown short term (next 24 h) PV power [25] 2013 atmospheric transmissivity, wind speed, relative humidity, sky cover 30 min from 1 January to 31 December 2012 unknown short term (day-ahead forecast) solar irradiation and PV power [26] 2016 NWP (not declared in a detailed manner) hourly unknown solar irradiation, weather condition unknown PV power [27] 2016 NWP (not declared in a detailed manner) hourly from 19 April 2014 to 28 February 2015 weather forecast short term (1–6 h) PV power [28] 2016 NWP (not declared in a detailed manner), cloud motion vector irradiations 15 min from March to November 2013 unknown very short term (15 min to 1 h) PV power [29] 2017 GHI, wind speed, air temperature, relative humidity hourly from 2009 to 2010 unknown short term (1 day ahead) PV power [30] 2017 daily total sunshine duration, daily average temperature, daily global solar irradiation unknown 1-year data unknown unknown PV power Table