Classical Seasonal Decomposition Research Articles

Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables.

Degradation assessment of two silicon photovoltaic technologies under subtropical desert climate

<span lang="EN-US">The prediction of performance of photovoltaic technologies is crucial, not only to improve the reliability and durability of these technologies but also to increase the confidence of investors and consumers in them. The accurate calculation of the degradation rate D<sub>R </sub>(%) in real operating conditions under specific climatic stresses is, therefore, paramount. The present study provides a comparison of performance losses of two silicon PV technologies installed on the rooftop of the Higher School of Technology in Laâyoune-Morocco. The two systems are a polycristalline array (pc-Si: 1.82 kWp) and an amorphous array (a-Si: 1.55 KWp), which are grid connected. In the light of related performance gathered over three-year, the degradation rates of the two systems were estimated using four statistical methods under the open-source software R. The techniques engaged in this paper are: classical seasonal decomposition (CSD), holt-winters (HW), autoregressive integrated moving average (ARIMA), and seasonal and trend decomposition by LOESS (STL). The results obtained using those methods show that DR(%) varies between 0.39% and 0.99% for pc-Si and between 0.29% and 0.64% for a-Si. The analysis of degradation accuracy shows that STL and CSD techniques provide results with high accuracy than ARIMA and HW for the two systems. The present study adds to knowledge on PV degradation under the subtropical desert climate of Laâyoune.</span>

Performance Loss Rates of a 1 MWp PV Plant with Various Tilt Angle, Orientation and Installed Environment in the Capital of Cyprus

Evolution from fossil fuel sources to environmentally friendly and sustainable energy sources is one of the key aspects of the energy transition goal. On the other hand, Solar Photovoltaic systems are the most promising and improving technologies in the energy market However, it is well known that the performance of PV panels decreases in the process of time. This paper focuses on analyzing the performance loss rates (PLR) of mid-scale crystalline silicon (c-Si) PV systems of the same manufacturer with different orientations and tilt angles in the same region for the duration of four years of outdoor exposure. Three commonly used statistical methods are deployed to calculate PLRs; seasonal and trend decomposition using locally weighted scatterplot smoothing (STL), classical seasonal decomposition (CSD) and year on year (YoY) methods coupled with various performance metrics, namely Performance ratio (PR), temperature corrected performance ratio TCPR) and weather corrected performance ratio suggested by National Renewable Energy Laboratory (NRELPR). It is found that PLRs of analyzed venues, Arazi, Arena and Stonite, with the YOY approach are −1.2%/year, −0.73%/year and −2.65%/year, respectivelty., respectively. Furthermore, STL method demonstrated PLRs of −0.69%/year, −0.65%/year and −2.91%/year, respectively. On the other hand, the CSD method generated positive PLRs for some inverters while aforementioned statistical approaches proposed PLRs close to −2%/year, making the CSD method inappropriate for use under a heavy soiling environment. Within two km distance, variations up-to four-fold in PLRs are recorded between the venues during analyzed period. Furthermore, variations in PLRs are more dependent on the statistical approach rather than the performance metric.

Performance degradation assessment of the three silicon PV technologies

AbstractThis study investigates seasonal performance and assesses the annual degradation rates (RD), of three types of silicon‐based PV module technologies, using four statistical methods, namely, linear regression (LR), classical seasonal decomposition (CSD), Holt‐Winters exponential smoothing (HW), and autoregressive integrated moving average (ARIMA) on a 5‐year temperature‐corrected DC‐performance ratio time series. The lowest performance degradation rates being exhibited are found for the polycrystalline silicon (pc‐Si) system with a RD values confined between 0.23%/year and 0.36%/year. The RD values provided by the four statistical methods to the monocrystalline silicon (mc‐Si) PV modules range from 0.40%/year to 0.77%/year. The values attributed to the annual performance degradation rate of PV modules of amorphous silicon technology typically range from 0.31%/year to 0.57%/year.

Long-term performance and degradation analysis of different PV modules under temperate climate

The share of solar photovoltaic in the energy mix is achieving significant growth worldwide. Accurate forecasts of return on investment and a good choice of optimal PV technology for different regions require proper PV studies. Long-term performance and degradation analysis, as well as an economic investigation of three based-silicon PV technologies including amorphous silicon (a-Si), polycrystalline silicon (p-Si), and monocrystalline silicon (m-Si) have been performed in this work. This study has been done based on different performance indicators such as AC energy production (EAC), reference yield (Yr), final yield (Yf), performance ratio (PR), temperature corrected performance ratio (PRSTC), degradation rate (Dr), and levelized cost of energy (LCOE). The results of the performance ratio analysis revealed that crystalline silicon technologies are better than amorphous PV system with values of 84.25% for m-Si, 84.32% for p-Si, and 79.14% for a-Si. The degradation analysis was performed using linear regression (LR) and classical seasonal decomposition (CSD) statistical techniques. The results of these latter indicate that a-Si degrades faster than its counterparts with a ratio of 0.9 ± 0.009%/year and 0.75 ± 0.003%/year, followed by m-Si (0.53 ± 0.01%/year and 0.41 ± 0.003%/year), and p-Si (0.36 ± 0.01%/year and 0.28 ± 0.004%/year) using LR and CSD, respectively. However, these degradation rates are comparable to other degradation rates obtained in studies performed in different climates in Morocco. Finally, the LOCE results demonstrate that p-Si technology is the most cost-effective system with a value of 0.09 USD/kWh compared to 0.1 USD/kWh for m-Si, and 0.13 USD/kWh for a-Si. According to the obtained findings, p-Si is the most optimal and suitable technology to be installed in the mountain temperate climate.

Research on Electricity Consumption Model of Library Building Based on Data Mining

With the exponential development of Chinese population, the massive energy consumption of buildings has recently become an interest subject. Although much research has been conducted on residential buildings, heating ventilation and air conditioning (HVAC), little research has been conducted on the relationship between student’s behavior, campus buildings, and their subsystems. Using classical seasonal decomposition, hierarchical clustering, and apriori algorithm, this paper aims to provide an empirical model for consumption data in campus library. Smart meter data from a library in Beijing, China, is adopted in this paper. Building electricity consumption patterns are investigated on an hourly/daily/monthly basis. According to the monthly analysis, electricity consumption peaks each year around June and December due to teaching programs, social exams, and outdoor temperatures. Hourly data analysis revealed a relatively stable consumption pattern. It shows three different types of daily load profiles. Daily data analysis demonstrated a high relationship between HVAC consumption and building total consumption, with a lift value of 5.9. Furthermore, links between temperature and subsystems were also discovered. Through a case study of library, this study provides a unique insight into campus electricity use. The results could help to develop operational strategies for campus facilities.

Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India

The performance and degradation of a 1 MWp utility-scale photovoltaic (PV) system located in the tropical semi-arid climate of India is investigated based on four years of monitored data. The reference yield, final yield, system efficiency, capacity factor, and performance ratio are 4.64 h/day 6.23 h/day, 11%, 19.33%, and 74.73%, respectively, according to the standard IEC 61724. The performance is compared to other large-scale PV systems in different climate conditions. The degradation of the PV plant is quantified by using various statistical methods. These methods include the linear least-squares regression (LLS), the classical seasonal decomposition (CSD), the Holt-Winters seasonal model (HW), and the seasonal and trend decomposition using loess (STL). The degradation rate is estimated at 0.27%/year, 0.32%/year, 0.50%/year, and 0.27%/year, respectively, after 50 months operating period. The degradation accuracy analysis classifies the LLS and HW as lower accuracy methods (0.22%) than CSD (0.11%) and STL (0.15%). A comparison of the degradation of mono-Si PV systems for various locations is performed using different statistical methods. This study contributes to the improvements in the knowledge of PV degradation in the Indian climate.

Field Analysis of three different silicon-based Technologies in Composite Climate Condition – Part II – Seasonal assessment and performance degradation rates using statistical tools

This paper is an extension study to the ‘Field Analysis of three different Silicon-based Technologies in Composite Climate Condition’ wherein, monthly average performance ratio, temperature corrected performance ratio, series resistance and effective peak power of multi-crystalline (mc-si), heterojunction with intrinsic thin layer (HIT) and amorphous silicon (a-si) based photovoltaic modules for three years were presented. The present study attempts to investigate seasonal performance and determine the degradation rates using three statistical tools, i.e. linear regression, classical seasonal decomposition (CSD), and locally weighted scatterplot smoothing (LOESS) on normalized performance ratio and normalized efficiency for three years. Further, comparative analysis of nine PV modules, i.e. three modules of each technology, and sensitivity analysis of solar irradiance and module temperature on performance ratio have been evaluated. The degradation rates are found to be 1.24%/year, 1.16%/year and 1.16%/year for a-si modules, 0.14%/year, 0.56%/year and 0.11%/year for HIT modules and 1.50%/year, 0.82%/year and 1.46%/year for mc-si modules using linear regression, CSD and LOESS analysis respectively. The average efficiency is found to be 5.17% for a-si, 15.40% for HIT and 10.78% of mc-si modules. Furthermore, the performance of these module technologies during particular season after three years of outdoor operation has also been assessed.

A study on the degradation rates and the linearity of the performance decline of various thin film PV technologies

In most available analyses of the annual degradation rate (RD, in %/year) of thin film PV (TFPV), light-induced degradation is not separated from long-term degradation and degradation linearity is virtually not explored.A study on the performance loss of various TFPV technologies in Jaén (Spain) is presented with a double goal: (a) providing more reliable values of RD by avoiding the approaches above and (b) assessing the suitability of some performance metrics and statistical techniques intended to estimate RD, depending on PV degradation linearity. Thus, monthly time series were constructed with PR DC, temperature-corrected PR DC and experimental peak power for two grid-connected TFPV technologies -a-Si and a-Si/µc-Si- and four TFPV modules -a-Si, a-Si/µc-Si, CdTe and CIGS- tested outdoors over a five-and-a-half-year and a six-and-a-half-year period, respectively. Classical seasonal decomposition (CSD) and Year-on-Year (YOY) statistical techniques were subsequently employed. The confidence intervals of RD found for the a-Si and a-Si/µc-Si PV fields overlap for CSD and YOY over the five-and-a-half-year measurement period and its sub-periods analysed, with RD ∼ 1.3%/year. This holds for the three performance metrics under scrutiny, a fact that is attributable to the closely linear degradation experienced by both PV fields. Mainly due to nonlinearities, CSD applied to any of the three performance metrics for the four TFPV modules fails to provide reliable values of RD. However, the values of RD obtained by applying YOY to such performance metrics for the aforementioned four TFPV modules over the five-and-a-half-year measurement period and its sub-periods studied are within the uncertainty range.

Review of Statistical and Analytical Degradation Models for Photovoltaic Modules and Systems as Well as Related Improvements

In this work, we investigate practical approaches of available degradation models and their usage in photovoltaic (PV) modules and systems. On the one hand, degradation prediction of models is described for the calculation of degradation at system level where the degradation mode is unknown and hence the physics cannot be included by the use of analytical models. Several statistical models are thus described and applied for the calculation of the performance loss using as case study two PV systems, installed in Bolzano/Italy. Namely, simple linear regression (SLR), classical seasonal-decomposition, seasonal- and trend-decomposition using Loess (STL), Holt–Winters exponential smoothing and autoregressive integrated moving average (ARIMA) are discussed. The performance loss results show that SLR produces results with highest uncertainties. In comparison, STL and ARIMA perform with the highest accuracy, whereby STL is favored because of its easier implementation. On the other hand, if monitoring data at PV module level are available in controlled conditions, analytical models can be applied. Several analytical models depending on different degradations modes are thus discussed. A comparison study is carried out for models proposed for corrosion. Although the results of the models in question agree in explanation of experimental observations, a big difference in degradation prediction was observed. Finally, a model proposed for potential induced degradation was applied to simulate the degradation of PV systems maximum power in three climatic zones: alpine (Zugspitze, Germany), maritime (Gran Canaria, Spain), and arid (Negev, Israel). As expected, a more severe degradation is predicted for arid climates.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naive one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naive and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors

In this paper, we discuss how extrapolation can be advanced by using some of the most successful elements and paradigms from the forecasting literature. We propose a new hybrid method that utilises: a) the decomposition approach of the Theta method, but instead of considering a linear trend we allow for nonlinear trends, b) rather than employing the extrapolation method on the raw data, we first apply smoothing to the data, and c) when seasonality is present, we employ the shrinkage approach to the derived indices instead of simply applying classical seasonal decomposition. We empirically evaluate the new proposition in the M3-Competition data with very promising results in terms of forecast accuracy.

Long term performance, losses and efficiency analysis of a 960 kWP photovoltaic system in the Mediterranean climate

This paper investigates the performance of a 960kWP photovoltaic (PV) system, located in southern Italy. Monitoring data over a 43months period are used to evaluate the monthly average of energy yields, losses and efficiency. The performance ratio and the capacity factor were 84.4% and 15.6%, respectively. The yearly average module efficiency was 15.3% and the system efficiency was 14.9%, whereas the capture and system losses were 0.6h/day and 0.1h/day respectively. The degradation rate of the PV system, after continuous outdoor exposure, has been estimated at about 1.48%/year by applying the Classical Seasonal Decomposition (CSD) method. A comparison in terms of degradation rate is made with other PV plants installed in different climates to address the need of standard methods to determine this crucial parameter. The actual performance of the studied PV system is compared with the expected outcomes by using two widely used PV simulation tools, SAM and PVsyst. A detailed difference analysis is carried out to evaluate the accuracy of the SAM and PVsyst models. Results show that the yearly average energy injected into the grid was under-estimated by 3.0% by SAM and by 3.3% by PVsyst, but overall PVsyst outperforms the SAM tool. Results provide reliable data on the plant and demonstrate its good performance compared to other plants located in the same climate. The results can be used to improve the prediction of the performance of future PV of systems in the Mediterranean climate, and also provide a useful dataset for comparative studies.

Analysis of photovoltaic system performance time series: Seasonality and performance loss

In this work, the seasonality and performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated through seasonal adjustment. The classical seasonal decomposition (CSD) and X-12-ARIMA statistical techniques were applied on monthly DC performance ratio, RP, time series, constructed from field measurements over the systems' first five years of operation. The results have shown that the RP of crystalline silicon (c-Si) technologies was higher during winter. This was also the case for the copper–indium gallium-diselenide (CIGS) and cadmium telluride (CdTe) technologies but with lower seasonal amplitude. The amorphous silicon (a-Si) technology exhibited a different seasonal profile, with high RP during summer and autumn and low during winter. In addition, the trends extracted from the application of CSD and X-12-ARIMA on three-year, four-year and five-year RP time series were used to estimate linear performance loss rates. A comparison between standard linear regression (LR), CSD and X-12-ARIMA has shown that CSD and X-12-ARIMA resulted in higher rates overall for c-Si, 1.07 and 0.93%/year respectively, but with significantly less uncertainty than LR. Lastly, it was shown that X-12-ARIMA provided statistical inference in the presence of outliers and produced model residuals that were uncorrelated, in contrast to CSD.