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Applying Data Science to Improve Solar Power Production and Reliability

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Worldwide demand for a reliable and sustainable supply of renewable energy, including solar, is growing. Accurate estimates of solar energy production and insights into solar equipment performance help solar plant owners and operators optimize inspections, schedule maintenance, improve the operational performance of their equipment, and maximize the environmental benefit of their investments in renewable energy. However, due to the uncertainties inherent in the unpredictable nature of this renewable resource, many challenges are associated with estimation of solar power production and detection of performance issues. In this study, our goal is to explore how predictions of solar inverter and plant production can be improved by applying data science techniques, and how machine learning models can be applied to correctly classify malfunction causes for solar inverters. Our results show that regional weather data can be used to estimate (and potentially predict) solar energy production for some applications; that a hybrid machine learning model based on historical data, temperature, and information from physical models outperforms predictions from state-of-the-art physical models; and that environmental factors such as lightning and ambient temperature, as well as grid operating conditions, can influence device reliability.
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Applying Data Science to Improve Solar Power Production and
Reliability
Mauricio Soto Karen Smiley Xiao Qu Travis Galoppo Rohini Kapoor
Alok Kucheria Melwin Jose
{mauricio.soto, karen.smiley, xiao.qu, travis.galoppo,
rohini.kapoor, alok.kucheria, melwin.jose}@us.abb.com
Abstract
Worldwide demand for a reliable and sustainable supply of renewable energy, including solar, is grow-
ing. Accurate estimates of solar energy production and insights into solar equipment performance help
solar plant owners and operators optimize inspections, schedule maintenance, improve the operational
performance of their equipment, and maximize the environmental benefit of their investments in renew-
able energy. However, due to the uncertainties inherent in the unpredictable nature of this renewable
resource, many challenges are associated with estimation of solar power production and detection of
performance issues.
In this study, our goal is to explore how predictions of solar inverter and plant production can be
improved by applying data science techniques, and how machine learning models can be applied to
correctly classify malfunction causes for solar inverters. Our results show that regional weather data can
be used to estimate (and potentially predict) solar energy production for some applications; that a hybrid
machine learning model based on historical data, temperature, and information from physical models
outperforms predictions from state-of-the-art physical models; and that environmental factors such as
lightning and ambient temperature, as well as grid operating conditions, can influence device reliability.
1 Introduction
In asset-intensive industries and operations, efficiently keeping critical long-lived operational equipment
healthy and performant is essential to mission success. Active asset performance management (APM) en-
ables customers to increase operational awareness of the health and performance of enterprise assets [10,25].
Heightened health awareness empowers customers to move from costly reactive maintenance towards risk-
based management techniques that optimize performance and maximize Return On Net Assets (‘RONA’) [32].
In this context, analytics have become essential for understanding and optimizing asset health and perfor-
mance.
With rising demand for a reliable and sustainable energy supply [29], solar energy production is playing a
crucial role in the residential, commercial, and industrial segments as well as for electric utilities. Penetration
of renewable energy is increasing around the world. As one example, in the first quarter of 2016, the added
generation capacity from solar to the U.S. grid represented 64% of the newly added generation capacity [17].
Optimization of solar power converters is critical in solar energy production: their failures account for 51%
of maintenance tickets in solar plants [30], and their performance is essential for maximizing solar production
under conditions which inherently have high variability [13].
An initiative was launched in early 2017 by a major manufacturer of solar power converters and related
equipment for generation of renewable energy. The goal of the initiative is to improve reliability, production,
and forecasting accuracy for solar production facilities. The manufacturer provided the authors with access
to over 20TB of real-world solar monitoring data, including metadata, telemetry (periodic measurements),
and events (machine and system states) associated with over 250,000 devices worldwide, as well as failure
data.
1
In this paper, we describe how we applied data science along with physics-based models to this real-world
data on solar production assets. First, we provide some background on the study content with respect to asset
health analytics and the solar industry, as well as an overview of related work. Three aspects of our study are
then described: better understanding and prediction of production of renewable energy (Section 2), improved
accuracy and lead time for detection of degradation and diagnosis of failures (Section 3), and making these
new analytics and the associated data readily accessible to end users and to internal customers who are not
data science experts themselves (Section 4). For each aspect, we identify the primary research questions, our
approach to the data science, and the results we have achieved to date. The paper concludes with a recap
of lessons learned and business benefits achieved, plus a view towards future work and related challenges.
Asset Health Key Performance Indicators (KPIs): To maximize the value of an asset, it
is helpful to predict as accurately as possible its behavior, production, events, risk of failure, and remaining
lifetime. While there are many ways to quantify health of an asset [27], most algorithms reflect on some
level the Risk of Failure (RoF) and the Remaining Useful Life (RUL). However, these health KPIs alone may
not reflect performance degradation, which can reduce production and RONA long before actual failure or
end of life. Accordingly, comprehensively managing asset performance requires analytics that quantify asset
degradation as well as production quantity and quality.
RoF is the statistical probability of failure of an asset at a point in time or over a time period. Un-
derstanding RoF (or more generally, the risk of an adverse event impacting performance) is highly impor-
tant. End of useful life is often aligned with a specific critical event of high interest. However, useful life
may be deemed ‘over’ prior to failure, e.g. if a device’s efficiency has dropped below an acceptable level.
Figure 1: Relationships among Degradation, RUL,
and RoF
Estimation of degradation, RoF, and RUL can en-
hance condition-based maintenance, prognostics, and
health management (Figure 1). However, these indica-
tors are highly challenging to estimate or predict. Fail-
ures can be identified as functional, design, process, or
random, and may be temporary or permanent. Deter-
mining useful values for degradation, RoF, and RUL
is a complex task involving various failure scenarios,
asset health, maintenance history, etc. A first esti-
mate of RUL can often be derived from manufacturer-
provided guidelines, then improved by using informa-
tion obtained via condition and health monitoring. Al-
gorithms are typically device-specific and incorporate
expert domain knowledge, as well as statistical and/or
machine learning techniques. Analytics may be ap-
plied predictively to drive proactive preventive main-
tenance decisions, or retrospectively to gain a better
understanding of failures that have already occurred.
Approach: One of the main challenges in so-
lar forecasting methods is developing new tools and
practices that manage the variability and uncertainty of solar power [29]. Our study leverages off-the-shelf
machine learning (ML) mechanisms applied to our corpus of data on real-world solar production equip-
ment together with physics-based models. Achievements from these solar analytics collaborations included
novel algorithms for benchmarking and forecasting solar inverter performance and reliability; algorithms for
real-time estimates of AC output and DC input power; automated diagnostic tools for service engineers for
analyzing events and telemetry; and new visualizations to help customers better understand (and gain more
value from) their solar equipment. The research also demonstrated how environmental data can augment
the business value of the analytics. These capabilities are now being integrated into the company’s portfolio
of solar monitoring and asset performance solutions.
Related Work: Due to the importance of reliability in energy supply, analysis of improvement in
production and reliability in power plants has been studied in the past [4, 12]. To mitigate the potential
risks of imbalances between supply and demand, the high variances in power generation in solar and other
renewable sources demand major attention to forecasting methods. Unpredictability in solar production can
be driven by many time-variant causes (e.g., changes in behind-the-meter self-consumption, the positions of
2
clouds, air pressure, or changes in temperature [31]). Accuracy of prediction for renewable energy is known
to vary according to its granularity, or time resolution. Previous studies [9, 14, 20,29] have demonstrated
that it is easier to achieve high accuracy in coarser granularity (e.g., day-ahead total daily production) than
in finer granularity (e.g., 5 minutes or 1 hour ahead). Data science applied to solar forecasting models [2,
3, 5–8, 11,15, 16, 18,21–24] is proving to be a powerful tool for analyzing asset data and characterizing asset
production, condition, and degradation; estimating RUL; quantifying RoF; and assessing the potential impact
of maintenance actions. As one example, Alanazi et al. [2] proposed a two-stage hybrid day-ahead solar
forecasting mechanism that introduces linear and nonlinear forecasting, therefore improving the accuracy
of the obtained results. Other previous efforts have analyzed photovoltaic and solar thermal electricity
generation from solar energy. Hoff et al. [13] presented a rigorous method to quantify power output variability
from a set of photovoltaic systems, and Martin et al. [20] produced statistical models based on time series
applied to predict half-daily values of solar irradiance by using auto-regressive neural networks and fuzzy
logic models.
To aid in selecting analysis approaches depending on the available data, previous studies [26] have classi-
fied algorithms relevant to the asset health concepts of degradation, RUL, and RoF. This work also developed
an assessment tool for characterizing “data readiness” for an analytics application, to provide valuable in-
sight for choosing which algorithms to apply, and catalogued various data imputation strategies for handling
missing values. These approaches were applied in this study.
2 Predicting Power Production
Models for solar power production1may be used in many scenarios. In this paper, we discuss two principal
instances:
1) Estimation of power production, based on factors such as seasonality and current irradiance, may be
used for detecting defects in the solar plant. Estimated DC or AC energy can be compared to the actual
values measured by inverters2during the same time period. Significant deviations or patterns may indicate
possible defects in the solar plant (e.g., degradation or sudden failure in one or more pieces of equipment,
from the panels to inverters).
2) Forecasts of power production may be used to reduce inherent uncertainties associated with variable
renewable energy generation. Grid operators today rely upon forecasts of both load [28] and generation to
balance electricity supply and demand. Accurate forecasts not only support the safe and reliable operation of
the grid, but also encourage cost-effective operations by improving the scheduling of generation and reducing
the use of costly ’spinning reserves’.
Towards improving the accuracy of power production estimation based on physical models, we identified
the following research questions:
RQ1: How do model results differ when using regional weather data, with lower temporal resolution,
vs. using data from hyperlocal weather stations with higher temporal resolution? (Section 2.1)
RQ2: Does combining physical and Machine Learning (ML) models improve accuracy for estimating
and predicting power production? (Section 2.2)
2.1 Impact of Weather Data on Prediction Accuracy
To address RQ1, we initially targeted all available solar plants which have one or more active inverters, in-
plant weather stations (with high geographic and temporal resolution), and for which we could obtain similar
weather data (lower geographic and temporal resolution) from a third-party source. In the manufacturer’s
monitoring system, data is collected from the in-plant weather stations, called Environment Units (EUs),
along with the inverters in solar plants. For this part of our study, we used two sets of historical weather
values: hyperlocal E U data from our corpus, and regional data from a third-party weather data source
(referred to as I). The third-party data was acquired through a limited quota of API calls that take the
1Our algorithms and models estimate and predict solar production in terms of DC energy for RQ1 and power for RQ2.
2The inverter measures DC and AC power. To be consistent with the outputs of our models for RQ1, we calculated values
for energy based on equation 7 from IEC 61724-1:2017, Section 9.4.2. [1]
3
coordinates of a plant as input. The IAPI calls return data from the weather station which is closest to the
plant location.
For each plant, we collected telemetry data for the inverters in the plants, which provide the actual
measurement of DC power as ‘ground truth’. We also collected the Global Horizontal Irradiance (GHI), and
the Ambient Temperature (AT) from both weather data sources (i.e., in-plant EU and the closest Iweather
station to the plant), in order to predict the power production based on various models. In this analysis, we
implemented the GHI -based physical model defined in IEC 61724-1:2017 [1]. Similar physical models and a
ML model were also introduced and studied for RQ2, as will be described in Section 2.2.
While the manufacturer’s monitoring of the inverters, and in-plant EU s typically captures data every
5-15 minutes, the Idata source currently provides regional weather data on at most an hourly basis. There-
fore, we used linear interpolation to fill in missing data points for the regional data. Figure 2 compares
Figure 2: Example of Actual DC Energy vs. Estimated (one day, one plant, one inverter)
actual DC input energy (E A) with estimated DC energy (Est E A GHI E U and Est E A GH I I ) for a
single solar plant and a single day. E A is the ‘ground truth’ read and calculated from a single inverter.
Est E A GHI E U is the estimated DC energy based on a GHI model using irradiance from the in-plant
EUs. Est E A GHI I is the estimated DC energy based on the same GHI model, where the input irradiance
data is from weather source I. The distance between the plant and the weather station of Iis 6.8 km. This
single-plant example with one day data illustrates the potential effectiveness of the GHI model for estimating
DC energy using either weather data source.
To answer RQ1, we repeated this analysis on multiple plants for a wide set of date ranges. We used the
mean absolute percentage error (MAPE) prediction accuracy method as the key metric, given its intuitive
interpretation in terms of relative error. After excluding inverters with invalid values for DC input signal
(i.e., missing E A) and EU s with invalid irradiance data (i.e., missing input for Est E A GHI E U ) for the
period 2016-01-01 to 2016-05-30, 53 plants were studied. The distances between plants and the closest I
weather stations were in the range of (2.36km, 11.87km).
Figure 3: MAPE distribution of DC energy esti-
mated by data from EU and IFigure 4: MAPE distribution of DC energy pre-
dicted by data from I
As shown in Figure 3, the median MAPE EU (actual DC energy vs. estimated DC energy based on
GHI measured by in-plant weather station EU , for each single inverter, averaged by all measured time) is
4
14.6%, and the median MAPE I (actual DC energy vs. estimated DC energy based on GHI provided by
weather station of I) is 20.7%, which is comparable to the error rate with in-plant weather stations. These
results indicate that, for some applications, predictions based on regional weather data may acceptably
represent daily power production in lieu of predictions based on in-plant weather data. This conclusion was
verified with domain experts in solar plant service. We also confirmed our results using BIAS, an alternative
prediction error metric. Currently, the prediction is applied on a single inverter. This meets the needs of
residential or small commercial applications with a single inverter, and also provides the granularity needed
to identify potential anomalies or degradation.
To explore the second scenario (forecasting power production, as described in the introduction of Sec-
tion 2), we performed an additional quick experiment with the Iweather data. Due to the limited quota of
API calls, we were only able to collect 7-day GHI forecasting from 2018-07-02 to 2018-07-08 for 14 plants
and studied 66 inverters in those plants. Distances between plants and the closest Iweather stations were in
the range of (2.27km, 16.38km). As shown in Figure 4, the median MAPE (actual DC energy vs. predicted
DC energy based on GHI forecast provided by I) is 16%. Although this was a highly constrained, short
experiment which precludes drawing any conclusion, the results are promising. Further exploration and more
data would be required to determine if power production can be forecasted accurately enough for a given
application by using third-party weather data.
2.2 Physics-based and Machine Learning models to estimate DC power
To address RQ2, we used 5-minute telemetry data for 2013-2017 from one inverter and its corresponding
EU to estimate DC power with physical and ML models. As introduced in the previous section, IEC 61724-
1:2017 [1] defines models that estimate the DC power produced by an inverter using the irradiance. Three
different physics-based models are commonly used to estimate DC power in watts. The first (’Clear Sky’)
model assumes that the sky is clear, and calculates irradiance based on latitude, longitude, time of year, and
other parameters. The second is based on GHI . The third and most accurate method is based on In-plane
Irradiance, or Plane of Array Irradiance (POA). We tested if ML can outperform the physical model by
leveraging time-series aspects of the data. Also, from the subset of data we examined, we noticed that
irradiance is often not measured on-site in solar plants. For those plants, we can only use the Clear Sky
model to estimate the DC power. As seen from Table 1, Clear Sky performs the worst out of the physical
models and is associated with very high errors.
Next, we trained a Long Short Term Memory (LSTM) network which takes advantage of the time series
aspect of our data. The input to this model consists of the 100 preceding DC power values and the output
of this network is an estimate for the current DC power output.
The inputs to this model can be seen in Figure 5. In this figure, P in(t) refers to the DC power predicted
at time t; P in(t1) refers to the measured DC power at time (t1); and so on.
Figure 5: Inputs to the Machine Learning
Model
Figure 6: Inputs to the Hybrid Model
5
We also built a hybrid model which combines ML with all 3 physical models. This model extends the
NN model shown in Figure 5 by using the current DC power estimates from the Clear Sky, GHI, and POA
models as additional inputs. The input parameters for the hybrid model are shown in Figure 6.
For all of the above experiments in section 2.2, we used data from one inverter of power 55KW. The data
was preprocessed as per guidelines introduced in IEC 61724-1:2017. We used a 7525 split for our train and
test sets so that we could test the model on substantial data volumes, and also compare with the physical
models. Accordingly, the NN models were trained using the initial 323K data points, and were tested with
107K data points. The same test set was used for the physical models.
To assess the quality of the models under inspection, we used Root Mean Squared Error (RMSE). This
metric tells us how far the estimates are from the actual values, and penalizes estimates which are further
away from the actual values. Lower values for RMSE correspond to a better fit of the model. The RMSE
values obtained on our test set are shown in Table 1.
Clear Sky Model GHI Model POA Model ML Model Hybrid Model
RMSE 14,581 6,655 6,354 5,370 4,590
Table 1: RMSE of various models
These results show how hybrid ML models based on physical models and additional features could be used
to estimate DC power more accurately than current state-of-the-art physical models. The results also show
that ML models can potentially provide much better estimation than the Clear Sky Model in the absence
of irradiance data. However, this is only a preliminary result: further validation with many more inverters
and plants is needed before any conclusions can be drawn.
3 Failure Diagnosis for Inverters
The previous section answered research questions on predicting solar power production and contrasting
current vs. predicted behavior of solar plants and inverters. For inverters which have already been determined
to not be working properly, we examined failure diagnosis by using off-the-shelf ML algorithms on a portion
of our corpus reflecting malfunctions in the inverters. In this section, we explore the following research
questions:
RQ3: How does lightning-related data correlate to inverter failure? (Section 3.1)
RQ4: Can historical telemetry data on inverters be used to diagnose failures? (Section 3.2)
3.1 Impact of Weather and Lightning on Failures
Since photovoltaic equipment (often including the inverter) is constantly exposed to atmospheric conditions,
identifying relationships between weather exposure and device failure is a critical step in developing more
resilient equipment and for understanding degradation, performance, or failures. For instance, field engineers
servicing solar inverters have noted that device failures seem to occur more frequently after intense lightning
storms. Based on this anecdotal evidence, we analyzed device failure rates over a 28-month period in an
effort to assess correlations between ambient weather conditions and inverter failure rates.
The data selected for this study included device age, geolocation, and communication timestamps from
over 100,000 monitored, globally-deployed solar inverters, along with hourly weather measurements in the
vicinity of each inverter (temperature, humidity, and precipitation), and global lightning strike data (times-
tamp, position, magnitude). For each device, the data was summarized as average monthly temperature,
humidity, and precipitation along with the number and intensity of lightning strikes occurring within a 15-
km radius for each month. In total, over 2.7 million device months and over 750 million cloud-to-ground
lightning flashes were available for analysis in our corpus. (The lightning data was graciously provided pro
bono by Earth Networks for this research.)
6
Figure 7: Left: Distributions of coefficient estimates resulting from Markov Chain Monte Carlo (MCMC)
sampling. The vertical line is at 0, showing temperature,humidity, and age * strike-count having significant
positive correlation with failure. Right: Pairwise scatter plots between coefficient estimates suggesting
collinearity between {temperature,precipitation}and humidity.
Figure 8: Y-axis shows the ratio of failure rates for
devices exposed to increasing lightning counts, by age,
with relation to similar age devices with average light-
ning exposure (ratio 1 is average exposure).
To investigate possible correlations between
weather exposure and device failure, we employed
a mixed-effects logistic regression with fixed effects
{age, temperature, humidity, precipitation, strike-
count, age * strike-count}, and per-model intercept
and strike-count random effects. The regression
was performed in an objective Bayesian framework
with coefficient estimation achieved via Monte-Carlo
sampling. Figure 7 shows the distributions of the
resulting coefficient estimates for each of the fixed
effects, along with pairwise scatter plots between co-
efficient estimates to help identify collinearity.
Our analysis shows temperature, humidity, and
age * strike-count all having statistically significant
positive correlation with device failure rates. With
respect to age * strike-count, this implies that as
devices age, the correlation between lightning ex-
posure and failure rate intensifies, suggesting, po-
tentially, that resilience to lightning-related failure
declines with age (or prolonged lightning exposure).
Figure 8 shows the correlation between lightning
exposure and failure rates by device age. The graph shows the ratio of failure rates with respect to average
lightning exposure; for instance, 3.5-year-old devices exposed to 100 (102) lightning strikes within a 15km
radius in a month fail at a rate approximately 40% higher than similar-age devices exposed to average (4)
strikes in a month.
Figure 9 shows similar plots for temperature and humidity, where average daily temperature is very
strongly correlated with increased failure rates. During any given month, devices exposed to an average
daily temperature slightly over 100oF fail at twice the rate of those exposed to an average daily temperature
of 77oF. Unfortunately, irradiance data (which could help identify if this is merely a function of increased
operation, or truly a relationship between ambient temperature and failure) was unavailable for the solar
plants used in this portion of the study. Nevertheless, this type of analysis can provide potential evidence of
weather-induced device failure which can be further investigated.
7
Figure 9: Failure ratios correlated with temperature (left) and humidity (right)
3.2 Impact of Non-Weather Conditions on Failures
Figure 10: Decision Tree Classifier.
Maxhmetricidiff refers to the maximum fluc-
tuation in the specified metric. H, B and F IN R
indicates the class of inverters: healthy, failure due
to booster error, and failure due to grid inrush.
‘value’ indicates the numbers of data points in these
three classes.
Diagnosing causes of device failure is a complex and
time-consuming task which often requires involvement
of a domain expert, which increases costs for the man-
ufacturer or servicer. Grid inrush is one infrequent,
but highly damaging, cause of premature failure which
is related to the environment in which an inverter
is operated. In this part of our study, we evaluated
whether grid inrush failures could be diagnosed from
monitoring data. We classified a subset of failures
from our corpus for a single inverter type by leveraging
data science, historical failure data, and the teleme-
try data generated by the device under consideration.
The monitoring data we used included the booster
temperature (TempBst), inverter temperature (Temp-
Inv), current (Igrid), voltage (Vgrid), and frequency
(Fgrid) readings from our monitoring data. We used
the results from failure diagnoses by service center ex-
perts to label our inverters according to three cate-
gories: Healthy inverters (H);inverters that failed due
to Booster error (B); and inverters that failed due to
grid INRush (F IN R). For the inverter subset we se-
lected for this study, we had 147, 40 and 24 inverters
respectively in these classes.
Figure 10 shows how a decision tree classified these inverters based on the monitoring (time series) data
captured prior to the failure. The hyper-parameters of the tree were selected by performing a grid search. The
model with the highest accuracy used the following hyper-parameter values: maximum depth=4, minimum
samples at leaf node=8, and minimum samples for a node to be considered for splitting=5. Using these
values, we obtained an accuracy of 84% in a 4-fold cross-validation, as an initial proof of concept. We then
used Random Forest to overcome the limitations of a single tree, which improved accuracy to 95%. Precision
and recall for the H, B and F IN R classes with the Random Forest model were (0.941, 0.986), (0.886, 0.775)
and (0.941, 0.986) respectively.
Even though this is a relatively small dataset, we consider that overall the analysis is potentially valuable
and might be generalizable to a broader dataset. This could enable creation of a service tool to diagnose the
cause for failure of a solar inverter by analyzing its telemetry data.
8
4 Visual Self-Service Analytics
In visual analytics, self-service Business Intelligence (BI) tools are employed to illustrate various data types
— including metadata, telemetry data, and event data — using figures, maps and other charts. The BI tools
can automatically fuse multiple data sources and types via common fields. Information is easily tailored for
different user requirements and interests through interactive filters and selections, and all customizations
made to one view are automatically applied to other views in real time. These features can help owners and
operators of solar plants and equipment to visually identify anomalies and obtain other insights efficiently
and effectively [26]. In this section, we highlight some ways we applied visual analytics tools3and techniques
to empower our users to benefit from the new solar analytics described in this paper.
Figure 11: Geographic distribution of solar plants and KPI visualization
Examples of such features are detailed in Figure 11. The left graph shows the distribution of a subset
of solar plants all over the world. Each circle represents a plant, and its size represents the number of a
certain type of devices (e.g., inverters) in the plant. Filters such as device model type, device state, and
region, are available for users. For all inverters in a selected group, several main KPIs and their changes are
calculated and illustrated, including Devices Under Management and Power Under Management. Each KPI
is visualized separately to show more details (Figure 11 right).
Figure 12: Asset timeline of telemetry and event data
for one inverter
In addition to providing an overview of a group
of devices filtered by different user requirements, vi-
sual analytics are also useful for understanding a
single device. For example, the concept of an asset
timeline [19] is to show a mixture of metadata, event
data, and telemetry data in a single composite visu-
alization to make it easier to see correlations. Figure
12 shows three days of telemetry and event data for
one solar inverter on the same timeline. We can eas-
ily see that one event (ID: 72549264) occurred right
after one telemetry data value (parsed ID: 6) was
captured. This indicates that it may be valuable to
investigate how suspicious values for some signals
(captured in the telemetry data) may correlate with
a subsequent error event.
Finally, we also provided visual tools to pinpoint
sub-optimally performing devices. For each device
for which historical telemetry data is available, we
generated an ideal performance curve by averaging
the device’s best historical performance. These ideal curves can be overlaid on the DC/AC power generated,
or any other metric of interest, to check the device performance in real time. Figure 13 shows how an ideal
curve (orange) for power generated is overlaid on the power produced by the inverter (blue) in real-time. Such
a tool can enable the plant owner or servicer to investigate under-performing devices and make adjustments
to achieve optimal power production.
3Qlik Sense (https://www.qlik.com/us/products/qlik-sense) and Grafana (https://grafana.com/)
9
Figure 13: Ideal curve overlaid on the actual AC power produced
5 Discussion
5.1 Conclusion
Our goal for these studies was to explore how predictions of solar inverter and plant health and production
can be improved by applying data science techniques. To date, our results have shown that predicting solar
AC production based on regional weather, rather than in-plant weather, can be sufficiently accurate for some
applications, and that blending physics-based models with ML and additional features can increase accuracy
of estimations of DC power production. These analytics enable detection of degradation and improvements
in production of solar energy. Similarly, our study shows that we can leverage Machine Learning and data
science to better understand and classify potential root causes of device malfunction such as lightning,
booster error, or grid inrush.
5.2 Business Value
Solar professionals are highly interested in minimizing Total Costs of Ownership (TCO). Inverter manu-
facturers can help meet their needs by offering analytics that provide early detection of degradation and
early prediction of failure. These analytics can provide valuable lead time for performing maintenance and,
if necessary, for acquiring and installing a replacement device or the right parts, to minimize maintenance
costs and prevent days of lost solar energy production. Actual monetary benefits are situational and can
be calculated based on factors such as days of lead time, whether hot spares are available, and avoidance
of multiple service trips by bringing replacements for the most-likely components. Other factors include
regulatory penalties and the value of electricity in the region (which ranges widely in the US and worldwide,
e.g. under $0.10/kWh in India or for industrial customers in North Carolina, or $0.33/kWh in Germany or
for residential customers in Hawaii).
5.3 Active Challenges and Future Work
In our ongoing work with solar analytics, we validate our assumptions and preliminary results, and consider
other types of solar analytics which may be of high value to solar customers and service providers. For
instance, so far we have assumed that the population of monitored inverters is representative of the population
of non-monitored inverters. That assumption can be systematically tested, and we intend to do so. In some
of the described use cases, a preliminary proof-of-concept was developed for a single inverter model or family.
Our future work includes expanding the analyses to multiple inverter models.
Going forward, we anticipate further incorporation of new modes of data. For instance, some work was
begun in 2017 and 2018 on natural language processing, using textual data which may be automatically
captured or manually entered in various languages (e.g. technicians’ diagnosis or repair notes); work on
leveraging this data is expected to continue. Use of other data modes, such as images from various sources,
is also being analyzed. We continue to leverage ‘intracloud’ data collection for solar inverters and plants to
create new analytics for benchmarking, forecasting, event and weather correlations, and self-service visual
BI. As they are developed, the analytics and tools described herein are being productized and deployed for
internal business and customer use in managing solar asset health and performance.
10
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