power factor prediction

1. Abstract:
The electricity operation is unique in terms of storage and supply
conditions. Therefore, forecasting (predictions) of the load demand
is great importance to measure (learn the system) the inconvenient
operation conditions. The prediction will provides insight into the
energy quantity required, so that the fluctuations may occur in the
energy demand can be controlled with proposing different solution
options. Also to prevent any problems with power supply (i.e
surplus / failure). There is many variables can be used to measure
the behavior of the system operation. One of the main variables
considered in power system operation is the power factor. The
electrical energy consumption cost and the power grid performance,
device rating and power regulation, industry product quality can be
evaluated by power factor. So the monitoring and prediction of
power factor is very necessary for that reasons. The methods of
measurements (with real time) are mostly cost and require more
time consuming fore interfacing with computer and analysis
(required high specifications of a computer system). To overcome
these limitations, artificial intelligence was introduced for parameter
prediction.
In this paper machine learning approach is used to predict the
variations of power factor in electrical power system of cement plant
factory. The aim of applying this technique is to replace the present
(more cost) of techniques of real time monitoring (additional cost).
Also can add more analyzes to improve the system performance e.g.
we make a second version of prediction after improving the power
factor for the same system. Also there is assisting in reducing the
error in the system and then maintenance costs, and can get more
reliable system. In addition and after the prediction if the power
consumed are to be plan to be constant then the economic
investment aspect can be developed accordingly and insure regular
supply operation with high efficiency.

2. Introduction:
Electrical plant use many equipment to implement the operation
work and it is necessary to determine which device or which part
need more energy demand and also which others need less energy
enough and give good quality and high power factor. This task can
be evaluated by monitoring the load consumption of the system for
long intervals by means of meters or may be with help of
microcontroller. Information about each part can give good
estimation for particular part. Power factor measurement can give
the good idea about the electrical system performance and evaluate
the amount of energy consumption.
Power factor is a ratio of useful power (working power) to the total
power (apparent power) supplied so it is a measure of the efficiency
with which electrical loads convert electrical power into useful
work. A high power factor is an indicator that the electrical loads are
utilizing power efficiently, while a low power factor indicates that
the connected electrical loads are utilizing power inefficiently. The
low power factor mean there is high power supplying but less active
power, i.e more current is wasted in network as reactive power, big
current mean more sizing cable and device rating and more
regulation. A poor power factor results in significant energy
wastage, and decreases the capacity of the electrical system.
A poor power factor due to induction motors, transformers, and
other inductive loads due to magnetic field created with this type of
load can be cause a lagging phase difference between current and
voltage at the terminals of an electrical load, or a distorted current
waveform. A poor power factor caused by distorted current
waveform is corrected by adding harmonic filters. A capacitor
corrects the power factor by providing a leading current to
compensate the lagging current. Power factor correction capacitors
are designed to ensure that the power factor is as close to unity as
possible. Although power factor correction capacitors can
considerably reduce the burden caused by an inductive load on the

3. supply, they do not affect the operation of the load. By neutralizing
the magnetic current, capacitors help to cut losses in the electrical
distribution system and reduce electricity bills.
However, collection and storage are only the first steps to make the
best use of the harvested data. Sophisticated data mining techniques
are required to establish patterns, trends, and outliers, leading to a
constructive analysis of past and present data diagnostics. Once the
ascertaining of data history and standings is complete, it allows for
future developments and the modeling of likely probabilities.
Ultimately this means practical actions taken now can lead to
tangible benefits later in time.
The historical data of power load are an ordered collection sampled and
recorded at a particular time interval, so they are a time series. As a branch of
artificial intelligence, soft computing technology aims to gain more reliable
and accurate systems and has proven to be an excellent tool for solving
various energy applications problems. We have used machine learning to
predict the integrated energy consumption of renewable and nonrenewable
power sources.
Generally, prediction of electric power demand can be classified according
criteria, range of applying time, and aim for the classification of electrical
power demand are summarized in Table 1. [10].
Table 1. Classification of electricity demand by four criteria.
Criteria Input Variables Aim
Long-term load
forecasting (LTLF)
Range of month Expansion planning of the
network
Medium-term load
forecasting (MTLF)
Range of weeks Operational planning power
generation
Short-term load
forecasting (STLF)
Range of day planning and dispatch cost
minimization
Very short-term load
forecasting (VSTLF)
Minutes or
hours
Scale of seconds to minutes
allows the network to
respond to the flow of demand
power generation

4. Contribution
Motivated by the aforementioned discussions, the main contributions of the
article can be summarized as follows:
 Critically evaluate and analyses literature of related forecasting,
prediction techniques in electrical systems.
 Collect and identify dataset of power factor measurements then
design and implement an external relational database model to serve
as a structured backup.
 Develop regression machine learning model to predict the daily
energy consumption (Random Forest Regressor, Decision Tree
Regressor, KNN Regressor).
 Create a neural network(three -stage architecture approach) that can
predict the recommendations of the algorithm.
 We propose the incorporation of the (characteristic function) loss
function in machine (deep) learning model learning for the benefit of
accurate prediction of the maximum consumption;
 Analyze outputs and results. Compare and contrast results against the
existing external measured data to make fitting.
 Repeat the analysis after Power factor improvements. Adapting the
concept of automatization, unmanned plant, and artificial intelligence
in the industry.
 To what extent can predictions be made from machine learning
techniques, so the fully understands the consumption efficiency
and to enable an efficient management.
Literature Review
In recent years, countries worldwide have actively researched the power field.
Deep learning technology has brought new opportunities and challenges to
power load forecasting [5]. The power system’s main task is to provide a safe
and reliable power supply for the consumers. Therefore, energy forecasting is
of considerable significance to the power field. Accurate power load
forecasting is of great importance for saving energy, reducing power
generation costs, and improving social and economic benefits. With the

5. development of power reform and the deepening of power marketization,
energy load forecasting has become more critical in the power system. It is
also essential to increase power demand forecasting accuracy for the power
system’s stable and efficient operation. Nonrenewable energy sources such as
coal, oil, natural gas, fossil fuels, nuclear, minerals, etc., cannot be regenerated
in a short period, and their consumption rate far exceeds their regeneration
rate.
Off-line technique is proposed in [3] to estimate performance characteristics
from motor parameters and manufacturer’s data. This performance
characteristics like current, speed, power factor, efficiency and torque from
the mathematical formulae relating with the equivalent circuit parameters.
Performance of motor has been presented into a set of output graphs. The
output graphs permit analysis of various motor parameters.
Energy prediction using soft-computing techniques plays a vital role in
addressing many challenges. As electricity consumption is closely linked to
other energy sources such as natural gas and oil, forecasting electricity
consumption is essential for making national energy policies In [4] paper,
various data mining techniques were utilized, including reprocessing historical
load data and the load time series’s characteristics. Then analyzing the power
consumption trends from renewable energy sources and nonrenewable energy
sources and combined them. A novel machine learning-based hybrid
approach, combining multilayer perceptron (MLP), support vector regression
(SVR), and CatBoost, is proposed in this paper for power forecasting. A
thorough comparison is made; taking into account the results obtained using
other prediction methods.
The author in [6] present a technique for predicting power factor variations in
three phase electrical power systems, using machine learning algorithms. The
proposed model was developed and tested in medium voltage installations and
was found to be fairly accurate thus representing a cost reduced approach for
power quality monitoring.
Yang et al. [9] have reported that the number of publications on the prediction
of electric power demand or consumption has been steadily increasing for 20
years, from eight in 1999 to 148 in 2018 [1]. Power demand forecasting for

6. the amount of electric power transaction EPT needs to be predicted by time,
day, month, year, and so on because the predicted value can be deferent
according to time scale. There are deep correlations among electric power
demand, amount of EPT, and optimal.
A model with [8] paper designated date, temperature and special day as
variables to predict the amount of electric power transaction (EPT) of the
Korea Electric Power company. They proposed single deep learning
algorithms and hybrid deep learning algorithms. The former included multi-
layer perceptron (MLP), convolution neural network (CNN), long short-term
memory (LSTM), gated recurrent unit (GRU), support vector machine
regression (SVR), and adaptive network-based fuzzy inference system
(ANFIS). Then selected a high-accuracy algorithm after measuring root mean
square error (RMSE) and mean absolute percent error (MAPE).
Even allowing for a drill down to a specific 60-second timeframe, knowledge
of seasonality trends and if the specific consumption is in keeping or out of
sync with long term trends is not available to us in Shapi et al. [20].
Nevertheless, not all studies factor in characteristics (i.e. weather and
building) and instead focus on a data-driven approach only as it argues that the
embodiment of these features are already encapsulated within the smart meter
data. One such research is Eneyew et al. 21], which seek to forecast the next
hour's consumption value through a DNN that comprises convolutional
features and a wavelet transform. One of the methodology segments involves
deploying a one-dimensional convolution model with max-pooling operations
to isolate and identify features from the dataset provided. After fitting the
model, the output layer will generate predictions of the consumption. Six
years' worth of hourly energy reads is used within the study across ten
different houses. Whilst some differences exist between the houses, there does
not seem to be any extreme outliers. The study uses the Mean Absolute
Percentage Error (MAPE) as a barometer of success through 3 interconnected
evolutionary experiments. A 1-dimensional dense model established
preliminary results.
The application of stationary wavelet transformation within a Long Short-
Term Memory(LSTM) convolutional model subsequently improved these
results. Goyal et al. [22] specializes their study in the impact systems such as
heating, ventilation, and air conditioning (HVAC) have on the overall energy
usage of a building and how the obligation to ensure there is adequate supply

7. can place extreme pressure on the supply grid. For this reason, high accuracy
forecasting is vital for the management of resources on an efficient level.
In [2] paper, a comparing of two different clustering techniques, K-means and
hierarchical agglomerative clustering is applied to real data from the east
region of Paraguay. Depending on the four raw data sets(feeders demand
consumption type) were to be pre-processed to obtain the load cureve of the
system , two clustering algorithms, two distance metrics and five linkage
criteria a total of 36 models with the Silhouette, Davies–Bouldin and
Calinski–Harabasz index scores was assessed. The K-means algorithms with
the seasonal feature data sets showed the best performance considering the
Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores
with a configuration of six clusters.
In [7] paper, the prediction of PPF using the ANFIS was conducted. Two
input variables, control rod position, and neutron flux were collected while the
PPF was calculated using TRIGLAV code as the data output. These input-
output datasets were used for ANFIS model generation, training, and testing.
In this study, four ANFIS model with two types of input space partitioning
methods shows good predictive performances with R2 values in the range of
6%e97%, reveals the strong relationship between the predicted and actual PPF
values.
Using nonlinear regression and fuzzy c-means clustering, Chen et al. [23] also
seeks to predict the subsequent day's energy consumption whilst factoring in
the trends which exist within the time-based characteristics. The study looks at
how features diversify and which factors influence this. Whilst the hypothesis
that a consistent energy pattern can play a crucial role in increasing the
accuracy levels of predictions is stated clearly from the outset; it is unclear
whether any new learning are gained from this other than the applicable
techniques themselves. As expected, incorporating weather statistics improves
the extrapolations. Prediction windows are predetermined and cover what is
described as "different typical seasons respectively". Revisiting the use of
data-driven techniques seen in Eneyew et al. [21], Yiyi et al. [24] looks at
predicting the daily usage of electricity in a residential dwelling for 17 months
from 2013 to 2015. The homes involved have indoor sensors to record

8. environmental settings to predict daily consumption through a 1-Dimensional
Convoluted Neural Network (CNN) and an LSTM recurrent solution. Outliers
were removed from the dataset with a Principal Component Analysis (PCA),
reducing the number of independent variables used in the study to 16.
By splitting the parameters between external weather variables and those
belonging to the internal building, the study focuses on areas of particular
interest. For example, the experiment selects only one house, and within that
one house, certain rooms are excluded due to low occupancy throughout the
days. Root Mean Square Error (RMSE) scores are impressive, but the study
hones in on very niche scenarios such as "only bedrooms" and "data without
bathroom inputs".
The development of the Smart Energy infrastructure has allowed better
recording and tracking of energy usage than ever before. Concurrently, there
is now the potential for future simulations with greater accuracy due to the
highly granular level of detail we now have. However, like all forecasting,
accuracy decreases as the timespan into the future increases that the
predictions are made. Nabavi et al. [25] identify the need to predict residential
and commercial energy demands for Iran by 2040.
prediction based on period of time
With respect to the time prediction there is many techniques, among various
techniques for predicting electric power demand, STLF is an essential
component of energy management systems (EMS) because it provides input
data for load flow and accidental analysis [3].
The authors in [1] present several solutions to implementation deep
learning algorithm (genetic algorithm two-stage approach) to predict
or forecast the exceeding level of loads (for a month time ) so the
capacity volume to be contracted in the following month can be
optimized, also the network charge for can be minimized. The
model was using multiple output artificial neural network prediction
to deliver significant benefits to customers. On other side the
maximum demands of customers are predicted with the deep
learning model (hybrid approach), simultaneously, to determine the
optimal capacity contract. In that paper a short-term load

9. forecasting (STLF) technique to forecast the maximum load taken
from the grid. The STLF technique usually aims to predict the load
up to one week ahead.
Hernandez et al. (2014) [11] have explained that weekly, daily, and hourly
forecasts are the most important forecasts. Among the four forecasts they
emphasize prediction of power demand for the next 24h because power
companies require accurate forecasting power demand.
Artificial intelligence including deep learning and machine learning has been
widely researched in various fields such as autonomous driving vehicle [12],
global horizontal irradiance [13], stock prices [14], wind speed [15], traffic
flow [16], and prediction of EPT amount and demand.
Various works has been surveyed and classified in [8]. A machine learning
techniques (MLTs) in [17] had been used to build a prediction model of the
electrical disturbances that may occur in the system. The proposed system is
used for features selection and classification of an open source electrical
disturbances dataset available online. Ant colony optimization is used for the
features selection and 5 MLTs are adopted for classification; k-nearest
neighbor, artificial neural networks, decision tree, logistic regression, and
naïve bayes.
Other related studies have researched short-term forecasting with different
approaches and various timelines used for the target prediction. Oprea [18]
forecasts the consumption of electric energy for the following 24 hours while
Khan et al. [19] focuses on several different periods in the near future for a
multitude of energy types. Short term forecasting can generally expect to
attain a high level of accuracy as predictions are likely to mirror present
trends. Khan et al. [19] focuses on the forecasting of power through a fusion
of 3 different machine learning approaches. As noted within the report,
"Combinations of prediction methods are receiving increasing attention".
Therefore ensemble learning is joining multiple different models to get a more
robust learning outcome. The study presents a combination of CatBoost with
both Support Vector Regresso (SVR) and Multilayer Perception(MLP). Each
model is trained independently, with the results concatenated for final
forecasting figure. Oprea [18] incorporates a NoSQL database (Mongo DB) to
merge energy consumption data from a residential building containing smart
meters with weather patterns of the same timeframe. The study looks at
implementing a feed-forward. Artificial Neural Network (ANN), with the
option of bench-marking against numerous other pre-existing algorithms.
During the initial analysis of the data, the factors that influence consumption,

10. such as weather characteristics, day of the week, and time of the year, are
given a ranking number representing how significant they are in the amount of
energy used within the building. Next, the application of K means clustering
partitions, customers into groups where the consumption levels are similar.
The aggregation of data into 24-hour values means that the consumption for
the building as a whole for the next 24 hours can be predicted.
It is interesting to note that whilst Oprea [19] analyses the building as a whole,
Shapi et al. [20] splits the study between 2 separate commercial type
customers and also drills down into the data as opposed to aggregating it.
Predicting energy consumption within a smart building begins with analyzing
the data collected from Internet of Things (IoT) meter sensors attached to
electrical sockets. The data can then be stored and examined on a granular
minute by minute level. The combination of K Nearest Neighbour(KNN),
Support Vector Machine(SVM), and an ANN within the methodology allows
the prediction of maximum demand based on electricity statistics, measuring
the spread of the distribution, and making forecasts based on predictions on
patterns mined within the data. The dataset is for a minimal amount of time
and only encompasses from June 2018 until December 2018 (for two different
tenants). Again this is in contrast to Oprea [18] whose study has a much more
comprehensive dataset containing over 6 million rows of data for 114 New
England based apartments with consumption readings also recorded on a
minute per-minute basis.
Time horizon selection. Numerous papers consider load forecasting, and
most of the works are mainly related to long-term optimization of the
electricity purchase and distribution process by suppliers and distributors. In
general, load forecasting has been investigated by utilities and electricity
suppliers, where long-term load forecasts (LTLFs) are used to predict the
annual peak of the power system [26] to manage future investments in terms
of modernization and launch new units to maintain the stability of nationwide
electricity demand over time periods of up to 20 years . Medium-term load
forecasts (MTLFs) use hourly loads to predict the weekly peak load for both
power and system operation planning [27]. Short-term load forecasts (STLFs)
usually aim to predict the load up to one week ahead, while very short-term
load forecasts (VSTLFs) are used for a time horizon of less than 24 h. Both
STLF and VSTLF have engaged the attention of most researchers since they
provide necessary information for day-to-day utilities’ operations [28]. These
forecasts also become useful when dealing with smart grids, microgrids, peak
load anticipation, and intelligent buildings [29].
Most recent works include some classical techniques, such as autoregression
(AR) models [30], linear regression models [31], seasonal ARIMA models,

11. which have been used to forecast load consumption [32]. Unfortunately, their
capability to solve time series with complex seasonality and nonlinear series is
limited, in favor of artificial neural network (ANN) techniques and expert
systems [33]. Interestingly, the load forecasting field is one of the most
successful applications of ANNs in power systems. Neural Networks are able
to deliver better performance when dealing with highly nonlinear series
resulting from, e.g., the non-integer seasonality appearing as a result of
averaging ordinary and leap years (365.25 days).
Feature extraction is another interesting approach. It entails proposing new
features from the original ones to enhance relevant information. In this
context, disregarding temporal information results in the loss of time-related
information and redundancy of features.In this context [34], Luo et al.
developed an integrated artificial intelligence-based approach that was
combined with an evolutionary algorithm to enhance an adaptive deep neural
network model. The proposal was tested on hourly energy consumption data.
Liang et al. [35] presented a hybrid model. Such model combined empirical
mode decomposition, minimal redundancy, maximal relevance and general
regression neural network with fruit fly optimization algorithm. This
approach, called EMD-mRMR-FOA-GRNN, was validated using load data
from the Chinese city of Langfang. Finally, a systematic time series feature
extraction method called hierarchical time series feature extraction was
proposed by Ouyanf et al. [36]. This model was used for supervised binary
classification tasks and only used user registration information and daily
energy consumption data to detect anomaly consumption users with an output
of stealing probability. The performance of this proposal was tested using data
from over 100,000 customers.
3. Machine Learning-Based Energy Load Forecasting
Machine learning is widely used in the energy sector for energy load
forecasting [18]. The machine learning method selects the load of the
past period of time as the training sample, constructs a suitable network
structure, and uses a particular training algorithm to train the network to
meet the accuracy requirements. Figure 1 shows the flow of our proposed
forecasting strategy.
Cement Industry – Case Study

12. Overview of the Cement Manufacturing Process
The manufacturing of cement generally involves four steps:
 Mixing
 Burning
 Grinding
 Storage
Mixing:
The mixing of raw materials can be done either with dry process or wet
process. . Both the materials are then channeled to mill equipment where they
are intimately mixed in desired proportions to form a paste.
Burning:
The process of burning is carried out in rotary kiln and the rotary kiln is lined
with refractory bricks. There is a temperature gradient inside the rotary kiln
with the lowest temperature being at the upper side and the highest
temperature being at the lower side.
Grinding:
The clinkers which are output from the rotary kiln are very hot hence they are
first cooled by air in a countercurrent fashion. The clinkers are fed into ball
mill or tube mill along with powdered gypsum. . Gypsum reacts with them to
produce tricalcium sulfo aluminates which is insoluble in water. They retard
the rate of setting of cement thus giving time for concrete placing.
Storage:
The grinded cement is then stored in silos. They are stored in 50 kg bags and
then shipped and marketed as container loads. Clinkers can also be marketed
and sold as per the requirement of the buyer.
Fig. 2 A schematic of the cement manufacturing process

13. Briefly, the process flow diagram of the cement industry is shown in “Fig. 2”.The
cement facility under this study is served by its demand via two feeders of 11 kV voltage
level. Each feeder is connected to the primary side of a step down transformer of rated 6.7
MVA supply the facility loads through their secondary side.
Power factor improvement :
To improve a low PF value, a power factor compensation (PFC) system
is usually applied [37–39] consisting of an electrical circuit that supplies
reactive power to the grid. Because of the voltage-current phase shift is
caused by high inductive loads, a capacitor bank or power electronics
converters (STATCOMs) are usually utilized to compensate and
improve the PF. Operation of these PFC is based on the connection /
disconnection of the PFC from the grid depending on real-time
measurements (smartmeter) of phase current and voltages waveforms. As
a consequence, this implies an increased complexity and cost for the PFC
system due to the need for a full sensor network required to monitor the
phase currents, voltages, and powers [40]. From the consumer side, it can
be necessary to use power quality analyzers for monitoring and recording
in real time the PF [41] implying high economical costs.
If PF variation could be predicted on a daily, basis it could be very
appealing, as no sensor network would be required for PF compensation
and the number of recorded electrical variables it could be minimized.
This minimization would simplify the monitoring procedure and reduce
the investment cost for the consumer. Evidently, this alternative
implemented by the consume
The artificial intelligence (AI) could provide a valid option to solve
issues concerning power quality and in particular about PF because in the
past few years it has been widely documented its influence in multiple
domains such as image processing [42], power electronics [43], medical
[44], and many other domains.

14. Artificial intelligence can be classified into different disciplines as
Computer vision…….
4.Methodology
In this work the electrical power system and actual energy consumption data
of cement plant(Lebda cement plant / libya) was selected for analysis of a
model for PF prediction, there is two feeder supply the system (11 kV) as
shown in fig (). The block diagram of the cement plant
The obtaining data for analysis from this plant was by means of three phase
analyzer (Vips),where the data are stored for(52 monitoring day with time
period of one hour for each day) in flash memory RAM. Recording is done
along with real time measurement for (each phase and for the total three
phases of each feeder) the power factor calculations [6]. Figures () shows the
measurement data where the PF data plotted as a function of measurement
time.
This purpose is to provide a reliable prediction of PF fluctuations by using
(ML) technique; in particular linear regression models have been used.
4.1 Machine learning algorithm
Once the data for each site was acquired, the procedure for ML analysis could
be performed.
Procedure for ML model building, testing and evaluating is graphically
depictedin Figure 5 and is the typical used in the literature [21]. First, datasets
are preprocessed (cleaning and tabular formatting), secondly site selection is
performed based on statistical results and data splitting for model training
using 70% of data for training and 30% of data for testing. Next, several linear
regression algorithms are used for training and the statistical results are used
to evaluate their performance. Finally, the model is tested in other selected
sites, and statistical results are analyzed for final model evaluation.
The purpose of any supervised ML model is to establish a function of the
predictors; that best explains the response variable (target). In this case, the
predictors are the phase currents and the target variable will be the power
factor value.

15. In our case, power factor data are continuous type therefore it is
recommended to use the regression methods, which in turn is divided
into different algorithms being OLS, Poly and RF the most important.
Below, a brief description of each algorithm is provided.
Decision Trees (RF)…….

16. Figure 3. Structure of the proposed hybrid model.
Results and Discussion
For this function to be stable and to be a good and reliable estimate of the
target variable, it is very important that these predictors are correlated with it.
Therefore, the first step would be to perform a correlation analysis between
these variables. The correlation is a statistical measure that indicates the extent
to which two or more variables move together.
A positive correlation indicates that the variables increase or decrease
together. A negative correlation indicates that if one variable increases, the
other decreases, and vice versa. The correlation coefficient (r) indicates the
strength of the linear relationship that might be existing between two
variables. A correlation map involving the phase voltages, currents and power
factor for every location was performed, and the results are shown in Figure 7.
It can be observed that the highest correlation was obtained between phase
currents and power factor whereas a weak correlation factor is observed

17. between phase voltages and PF. Therefore, the use of only phase currents to
predict PF is justified.
Specifically, the good performance of any ML model relies upon data
distribution and for linear regression models four main characteristics should
be taken into account: additively and linearity of effects, constant error
variance, normality of errors and zero correlation between errors. Therefore,
for ML applications it is always preferable to have a normal (gaussian)
distribution as described by Equation (2):
 ML normality
 decision maker
 Data Visualisation( Histograms and KDE analysis)
 Training and mean square Errors
 Evaluation and Results of Developed Models
Fitting results discussion
The plots in Figure 10 show a rather good fit between model predicted data
and actual measured PF values. These results validate the satisfactory
performance of the proposed model where only phase currents were taken into
account.
Power factor improvement:
Conclusion
In this work a new approach to predict power factor variations has been
proposed relaying only on phase currents (without considering phase voltages)
thus simplifying thedata acquisition procedure and consequently reducing the
time and costs for a simplified power quality analysis at consumer facilities. It
also was shown that Random Forest model gives a very good result for
different sites (with different electrical loads). The root Mean Square Error
and the coefficient of determination obtained were quite acceptable. The

18. prediction results demonstrate the viability for use this model for PF variations
prediction using only phase currents as input variables in power systems
where the PF reflects the power consumption from the grid. Finally, the
developed model can be modified to adequately predict PF variations when
phase currents do not show a high correlation as a result of specific
installation conditions and to consider the presence of grid-connected
renewable energy sources.
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