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Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities

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Abstract

Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the datasets obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis.
Forecasting Solar Power Generation on the basis of
Predictive and Corrective Maintenance Activities
Soham Vyas
Department of Computer Science &
Engineering, PDEU, Gandhinagar,
India, Soham.vmtds21@sot.pdpu.ac.in
Sanskar Bhuwania
Department of Computer Science &
Engineering, PDEU, Gandhinagar,
sanskar.bce18@sot.pdpu.ac.in
Brijesh Tripathi
Department of Solar Energy, PDEU,
Gandhinagar, India,
brijesh.tripathi@sot.pdpu.ac.in
Yuvraj Goyal
Department of Computer Science &
Engineering, PDEU, Gandhinagar,
India, Yuvraj.gmtds21@sot.pdpu.ac.in
Hardik Patel
Department of Information and
Communication Technology, PDEU,
Gandhinagar, India,
Hardik.patel@sot.pdpu.ac.in
Neel Bhatt
Department of Computer Science &
Engineering, PDEU, Gandhinagar,
India, Neel.bmtds20@sot.pdpu.ac.in
Shakti Mishra
Department of Computer Science &
Engineering, PDEU, Gandhinagar,
India, Shakti.mishra@sot.pdpu.ac.in
AbstractSolar energy forecasting has seen tremendous
growth in the last decade using historical time series collected
from a weather station, such as weather variables wind speed
and direction, solar irradiance, and temperature. It helps in the
overall management of solar power plants. However, the solar
power plant regularly requires preventive and corrective
maintenance activities that further impact energy production.
This paper presents a novel work for forecasting solar power
energy production based on maintenance activities, problems
observed at a power plant, and weather data. The results
accomplished on the dataset obtained from the 1MW solar
power plant of PDEU (our university) that has generated data
set with 13 columns as daily entries from 2012 to 2020. There
are 12 structured columns and one unstructured column with
manual text entries about different maintenance activities,
problems observed, and weather conditions daily. The
unstructured column is used to create a new feature column
vector using Hash Map, flag words, and stop words. The final
dataset comprises five important feature vector columns based
on correlation and causality analysis.
Further, the random forest regression is used to compute the
impact of maintenance activities on the total energy output. The
causality and correlation analysis has shown that the five feature
vectors are interdependent time series variables. Next, Vector
Autoregression (VAR) is chosen for simultaneous forecasting of
total power generation for 3, 5, 7, 10, 12, and 30 days ahead using
the VAR model. The results have shown that the root means
square percentage error (RMSPE) in total power generation
forecasting is less than 10% for different days. This research has
proven that the spikes in total power generation forecasting can
be traced and tracked better using daily maintenance activities,
observed problems, and weather conditions.
Keywordsforecasting, vector autoregression, maintenance
activities, solar power generation, weather conditions
I. INTRODUCTION
Solar power generation has the potential to mitigate climate
change by reducing the carbon footprint. It has had better
market penetration in recent years because of awareness
about clean and green energy and its affordable cost. Solar
power plants require various planned and unplanned
maintenance activities for better energy output. These
maintenance activities include PV module cleaning and
maintenance, PV module positioning in the field, inverter
maintenance, etc. Solar energy forecasting is usually done
using past time series data acquired from weather stations
such as wind pressure, humidity, temperature, satellite
imagery, etc. In this research work, total solar power
generation forecasting is proposed by using different
maintenance activities, problems observed, and weather data.
Next, we have carried out a literature survey to understand
the contemporary work done in this area.
Fuzzy logic, AI models, and genetic algorithms are used to
predict and model solar radiation, seizing, performances, and
controls of the solar photovoltaic (PV) systems in [1].
Ensemble of deep ConvNets is proposed for multistep solar
forecasting without additional time series models like RNN
or LSTM and exogenous variables in [2] with 22.5% RMSE.
Mycielski-Markov is utilized to forecast solar power
generation for a short period in [3] with 32.65% RMSE.
Feedforward neural network-based solar irradiance
prediction is followed by LSTM-based solar power
generation prediction for a short period [4] with 98.70
average RMSE. The ensemble approach is proposed based on
long short-term memory (LSTM), gated recurrent unit
(GRU), Autoencoder LSTM (Auto-LSTM), and Auto-GRU
for solar power generation forecasting in [5] without
considering any maintenance activities. Generic fault/status
prediction and specific fault prediction by unsupervised
clustering and neural network by using data of 10MW solar
power plant and one hundred inverters of three different
technology brands [6]. This model can predict generic faults
up to 7 days in advance with 95% sensitivity and specific
defects before some hours to 7 days [6]. Intra hour, short
term, medium term, long term, ramp forecasting, and load
forecasting are proposed for renewable EnergyEnergy like
wind and solar EnergyEnergy [7]. Solar power generation is
reduced by 17.4% per month because of dust on solar
collectors [8]. Day-ahead forecasting of 1MW solar power
plant output is proposed in the American Southwest with
10.3% to 14% RMSE [9]. Solar power generation is forecast
using different neural network models like LSTM, MLP,
LRNN, feedforward, ARMA, ARIMA, SARIMA, and 3640
hours of data for a 20MW power plant [10]. Six-hour-ahead
solar power forecasting is proposed using an autoregressive
forecasting model at residential and medium voltage
substation levels [11]. The autoregressive model of [11]
claims 8% to 10% improvements in results. Two-stage
probabilistic solar power forecasting is proposed in [12], the
first stage is used to predict solar irradiance, and the second
stage is used to predict solar power. The model of [12] results
in minimum loss and the highest daily profit in the energy
market. A robust auto encoder-gated recurrent unit (AE-
GRU) model is used to forecast solar power generation for 24
h, 48 h, and 15 days [13]. Sparsity promoting LASSO-VAR
structures are proposed and fitted with alternating direction
method of multipliers (ADMM), 1hour and 15-minute
resolution for solar power forecasting in [14]. The LASSO-
VAR model of [14] improves 11% in the forecasting. The
probabilistic solar power forecasting is proposed and
compared with the autoregressive method in [15], which
results in RMSE of 8% to 12%. A nonlinear autoregressive
neural network with an exogenous input model is proposed
with Levenberg-Marquardt, Bayesian regularization, scaled
conjugate gradient, and Broyden-Fletcher-Goldfarb-Shanno
(BFGS) algorithms for solar power forecasting over
NIGERIA [16]. The models of [16] result in RMSE values
ranging from 0.162 to 0.544 W/m2. Five-minute-ahead
forecasts are produced and evaluated using point and
probabilistic forecast skill scores and calibration using sparse
vector autoregression for 22 wind farms in Australia [17].
The LASSO vector autoregression model is proposed for very
short-term wind power forecasting [18]. A vector
autoregression weather model is proposed for electricity
supply and demand modeling with six hours ahead
forecasting with less RMSE [19]. Graph-convolutional long
short-term memory (GCLSTM) and the graph-convolutional
transformer (GCTrafo), named two novel graph neural
network models, are proposed for multi-site photovoltaic
power forecasting with 12.6% and 13.6% NRMSE
respectively [20]. The following sections are data set
preprocessing, methodology, results and analysis,
conclusions, and future work.
II. DATA SET PREPROCESSING
Pandit Deendayal Energy University (PDEU),
Gandhinagar, and Gujarat Energy Research and Management
Institute (GERMI) set up a 1 MW Solar Power plant in 2012.
The dataset obtained from this solar power plant has been used
for this work from 2012-to 2020. This dataset has daily entries
of 13 columns from 2012 to 2020. The solar plant consists of
five sets of PV modules. Three out of these five sets are
"poly-crystalline" based, and each has the capacity of
approximately 250KW. The remaining two PV modules are
"thin-film amorphous silicon and Concentrate
Photovoltaicbased with capacities of approximately 250
KW and 15 KW, respectively. There are four sets of PV
modules, and each set has approximately a 250KW capacity.
The fifth set of PV modules has approximately a 15KW
capacity. The dataset has five columns for power generation
from five sets of PV modules and the other columns are
“date”, “Total power generation (KWH), aggregate meter
reading (KWH), difference, Seeds data (KWH),
insolation, PR (%) and “any issues/problems observed".
As discussed above, there are 13 columns in this data set, and
it is semi-structured because the last column, "any
issues/problems observed," has text data that includes day-
wise manually entered weather information, maintenance
issues, grid failure, module cleaning information, etc. from
2012 to 2020. The first and most important research challenge
is to create the different features from the last column, "any
issues/problems observed ."This research challenge was
addressed by creating a nested hash map with different rules.
The key contains the possible feature label as a text, and the
value is a 2-dimensional array. One array has words
representing the maintenance issues, a problem observed, or
weather conditions. The second array has to stop words that
prevent overlapping and duplication of the maintenance
issues or problems observed or weather conditions. Each key
is the new feature (maintenance or problem observed or
weather condition) column, and the value is tokenized as one
of the new features is present on a particular day. New feature
vectors are created with labels from the column "any
issues/problems observed ."Now, each new feature column
vector label value one is replaced by its percentage of the
occurrence. New feature vectors created are "Grid Failure”,
Inverter Failure”, “Module Cleaning”, “Rainy Day”, “No
Module Cleaning”, Transformer Replacement and
Maintenance”, Cable and Fuse Maintenance”, Plant
Shutdown,", "Internet”, “Battery”, “Cloudy day”, “Module
Cleaning by Rain by using the above approach. There are
only five columns, “Total generation (KWH)”, Grid
Failure”, Inverter Failure”, Module Cleaning", and
"Cloudy," in the final dataset based on the correlation and
causality analysis. Vector autoregression (VAR) model is
selected for simultaneous forecasting of total power
generation and new features because they are inter-dependant
time series data.
III. METHODOLOGY
In this paper, solar power generation is forecasted using
maintenance activities. It is novel work, and there is not much
research done on this topic. The power generation prediction
is formulated as a regression problem to understand the usage
of maintenance issues. The labels of processed datasets have
been used to feed the regression model, and the future
maintenance variable has been considered test data. Random
Forest Regression is applied to this data set, and it has been
observed that the maintenance issues can be used as variables
to forecast the power generation.
Fig. 1 Regression Model using Random Forest Regressor
Vector Autoregression
VAR models are used for multivariate time series. The VAR
models consider each variable as a linear function of past lags
of itself and past lags of the other variables. Five variables
<Total Generation (KWH), Grid failure, Inverter Failure,
Module Cleaning, and Cloudy > have been considered and
modeled as a system of equations with one equation per
variable in time series. Let us consider if we have two
variables (Time series), Y1 and Y2, and we need to forecast
the values of these variables at a time (t). To calculate Y1 (t),
VAR will use the past values of both Y1 and Y2. Likewise, to
compute Y2 (t), the past values of both Y1 and Y2 are used. For
example, the system of equations for a VAR model with two-
time series (variables `Y1` and `Y2`) is as follows:
1, 1 11,1 1, 1 12,1 2, 1 1,
2, 2 21,1 1, 1 22,1 2, 1 2,
t t t t
t t t t
Y Y Y
Y Y Y
 
 


 
 
(1)
The vector autoregressive model of order one is denoted as
VAR (1). Similarly, in a VAR (2) model, the lag two values
for all variables are added to the right sides of the equations.
In the case of five Y-variables (or time series), there would
be ten predictors on the right side of each equation, five lag
one term and five lag two terms. For a VAR (p) model, the
first p lags of each variable in the system would be used as
regression predictors for each variable. As per equation (1),
the data follows stationarity and the causality test. In a
causality test, the data follows the interconnected time series
dependencies. Akaike information criterion (AIC) is the
model Mk with dimension k is defined as where L (Mk) is the
likelihood corresponding to the model Mk. The first term in
AIC is twice the negative log-likelihood, which turns out to
be the residual sum of squares corresponding to the model Mk
for the linear regression model with a Gaussian likelihood
[21]. AIC has been computed using data before forecasting,
and optimal AIC was derived for the specific lag days to fit
the VAR. After checking the model with acquired lag day,
the coefficient matrix is computed for each equation. Here
five variables are used for the endogenous attribute.
Forecasting results with multiple periods of days will give us
an understanding of how power generation varies and the
probability of the various spikes. Our equation has
coefficients for years and tries to forecast with the help of lag
days. It will become our return value, and the result will be
separated into multiple scenarios.
IV. RESULTS AND ANALYSIS
The VAR model is given to understand the effect of different
days on total power generation forecasting results for
different days.
Fig.2 Three days ahead of total power generation forecasting by using the
VAR model
As shown in fig.2, the RMSPE, RMSE, and MAE are 3.38%,
130.414, and 118.249, respectively, for three days ahead of
total power generation forecasting.
Fig.3 Five days ahead total power generation forecasting by using the VAR
model
As shown in fig.3, the RMSPE, RMSE, and MAE are 5.52%,
206.331, and 183.795, respectively, for five days ahead of
total power generation forecasting.
Fig.4 Seven days ahead total power generation forecasting by using the VAR
model
As shown in fig.4, the RMSPE, RMSE, and MAE are 6.27%,
253.774, and 207.842, respectively, for seven days ahead of
total power generation forecasting.
Fig.5 Ten days ahead total power generation forecasting by using the VAR
model
As shown in fig.5, the RMSPE, RMSE, and MAE are 5.91%,
235.562, and 187.288, respectively, for ten days ahead of
total power generation forecasting.
Fig.6 Twelve days ahead total power generation forecasting by using the
VAR model
As shown in fig.6, the RMSPE, RMSE, and MAE are 5.49%,
218.91, and 169.183, respectively, for 12 days ahead of total
power generation forecasting.
Fig.7 Thirty days ahead total power generation forecasting by using the VAR
model
As shown in fig.7, the RMSPE, RMSE, and MAE are 9.59%,
394.128, and 309.921, respectively, for 30 days ahead of total
power generation forecasting.
TABLE I. RMSPE, RMSE, AND MAE IN TOTAL POWER GENERATION
FORECASTING FOR DIFFERENT DAYS.
Days
RMSPE
RMSE
MAE
3
3.38
130.414
118.249
5
5.52
206.331
183.795
7
6.27
253.744
207.842
10
5.91
235.562
187.288
12
5.49
218.91
169.183
30
9.59
394.128
309.921
As shown by figures 2 to 7, the VAR model can predict
almost all power generation spikes, which is the most crucial
point of this research. The total power generation spikes are
due to different maintenance activities, problems, and
weather conditions. The real power generation forecasting
error is lowest three days ahead of forecasting. Table I shows
that the error in total power generation forecasting is less than
10% for different days. VAR model can forecast all the new
features Grid Failure”, Inverter Failure”, Module
Cleaning”, Rainy Day”, No Module Cleaning”,
Transformer Replacement," and Maintenance”, “Cable and
Fuse Maintenance”, “Plant Shutdown”, “Internet”, “Battery”,
Cloudy day”, “Module Cleaning by Rainand “total power
generation” because all these are interdependent time series.
V. CONCLUSIONS
The research work in the paper presents the forecasting of
total power generation based on various maintenance
activities carried out in solar power plants. Scheduled
maintenance activities in the power plant impact energy
production. This work involves transforming the unstructured
dataset into structured form with twelve new feature vectors
using HashMap, flag words, and stop words. Further,
Random Forest Regressor is used to analyze the impact of
maintenance activities on forecasting total power generation.
The same outcome has shown that the total power generation
prediction is perfect because of the maintenance activities.
The maintenance activities are not available for forecasting,
so maintenance activities should be predicted before the total
power generation forecasting. Vector Auto Regression-based
model is used for forecasting multivariate time-series
considering five variables “Total Power Generation (KWH),
Grid Failure, Inverter Failure, Module Cleaning, and
Cloudy. VAR can forecast total power generation along
with forecasting four maintenance activities. The three days
ahead total power generation forecasting has the lowest error
compared to other results. Total power generation forecasting
is implemented in two stages in the literature review. The first
stage predicts solar irradiance or maintenance activities,
problems, and weather conditions, and the second stage is
total power generation forecasting. In this research work,
forecasting of solar power generation and maintenance
activities, problems, and weather conditions are all done
simultaneously.
VI. FUTURE WORK
In the future, this work shall be extended by comparing the
total power generation forecasting using different models and
the inclusion of two essential feature vectors, solar
irradiance and insolation," in the current VAR model for
solar power generation forecasting. It is also planned to
forecast the solar power generation for an individual set of
PV modules to determine the impact of different PV modules
on the forecasting. The evaluation shall be based on the
overall effect of varying PV modules, "solar irradiance",
"insolation," and daily maintenance activities, problems
observed, and weather conditions on the total power
generation forecasting.
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