Conference PaperPDF Available

Unsupervised Learning of Representations from Solar Energy Data


Abstract and Figures

In this paper, we propose an unsupervised method to learn hidden features of the solar energy generation from a PV system that may give a more accurate characterization of the process. In a first step, solar radiation data is converted into instantaneous solar power through a detailed source model. Then, two different approaches, namely PCA and autoencoder, are used to extract meaningful features from the traces of the solar energy generation. We interpret the latent variables characterizing the solar energy generation process by analyzing the similarities of 67 cities in Europe, North-Africa and Middle-East through an agglomerative hierarchical clustering algorithm. This analysis provides also a comparison between the feature extraction capabilities of the PCA and the autoencoder.
Content may be subject to copyright.
Unsupervised Learning of Representations
from Solar Energy Data
Nicola Piovesan, Paolo Dini
CTTC/CERCA, Av. Carl Friedrich Gauss, 7, 08860, Castelldefels, Barcelona, Spain
{npiovesan, pdini}
Abstract—In this paper, we propose an unsupervised
method to learn hidden features of the solar energy
generation from a PV system that may give a more
accurate characterization of the process. In a first step,
solar radiation data is converted into instantaneous solar
power through a detailed source model. Then, two different
approaches, namely PCA and autoencoder, are used to
extract meaningful features from the traces of the solar
energy generation. We interpret the latent variables char-
acterizing the solar energy generation process by analyzing
the similarities of 67 cities in Europe, North-Africa and
Middle-East through an agglomerative hierarchical clus-
tering algorithm. This analysis provides also a comparison
between the feature extraction capabilities of the PCA and
the autoencoder.
Index Terms—Energy sustainability, neural networks,
PCA, solar energy, unsupervised learning
In recent years, several articles have shown that the
energy consumption of ICT may represent a problem
in terms of sustainability. In a report of 2013, the
Digital Power group [1] has calculated that 10% of the
worldwide electricity generation is due to ICT, which
is exceeded by more than 50% by that of the avionic
industry. A forecast for 2030 estimates that 51% of the
electricity consumption and 23% of the carbon footprint
by human activity will be due to ICT [2]. This trend
is mainly driven by new infrastructure deployments to
provide mobile services.
The introduction of renewable energy sources is today
one of the most promising approaches to reduce the
greenhouse gases emissions due to the human activities.
In particular, the reduced cost of the harvesting devices,
e.g., Photovoltaic (PV) systems, makes solar energy one
of the most interesting sources among the renewables.
Several works in the literature [3], [4], [5] discuss the
integration of solar energy sources into communication
networks as a way to reduce their energy consumption
and conclude that such an integration is feasible only
with the introduction of an intelligent control system
able to manage the intermittent and erratic energy budget
from renewable sources.
One of the key factor determining the performance
of a PV system and its potential application to supply,
e.g. communication network devices, is the solar energy
arriving at the surface of the Earth. The exploitation of
the solar energy resource is determined by the knowl-
edge of geographical variability and time dynamics. The
geographical analysis of the availability of the primary
solar energy resource is then essential to understand
the potential implementation of PV systems for future
energy supply to a specific industrial sector. The ge-
ographical dependency and distributed nature of solar
electricity generation impose questions that require spe-
cific location-dependent answers. In fact, the harvested
energy strictly depends on the seasons of the year and
the meteorology of the given location. In [6], it has been
estimated that, in the same site, even during summer and
in good weather conditions, the harvested energy in the
peak irradiation hour can vary up to the 85%. Similarly,
considering that the solar radiation intensity and the
daylight duration vary significantly across the months
[6], seasons have a strong impact on the amount of the
harvested energy income. Normally, this geographical
analysis is performed using solar maps, which provide
easy-understandable information. The total annual solar
electricity generation from a PV system is used to
characterize national and regional differences [7]. In
this paper, we are interested in learning hidden features
of the solar energy generation that may give a more
accurate characterization of the process beyond that
usual metric.
Recently, representation learning has received signifi-
cant attention as a highly effective alternative to conven-
tional feature sets handcrafted by a domain expert [8].
These techniques have been shown to be superior to fea-
ture engineering for a plethora of tasks, including speech
recognition, music transcription, audio and video recog-
nition [9]. However, these methods suffer the big issue
of the interpretability of their results [8]. In this work,
we use unsupervised representation learning methods to
extract automatically time-dependent features from solar
electricity generation data and provide a meaningful
interpretation of the results achieved. Although machine
learning methods have been used in renewable energy
modeling, they have been adopted mainly to forecast the
next energy arrivals in a given location [10]. Instead, to
the best of our knowledge, they have not been used for
analyzing geographical differences in solar electricity
In this paper, we state the geographical analysis of
the availability of the solar energy generation from a
PV system as an unsupervised learning problem due to
the lack of ground truth for the considered scenario.
In detail, raw data from a real database describing the
solar irradiance on a plane surface in 67 different cities
are processed to obtain estimations of the instantaneous
solar energy generation. We introduce an astronomical
model that allows estimating the actual solar electricity
generation by a PV system. Then, an electrical model of
the PV module and of the DC/DC power processor are
used to provide the final estimation. Feature extraction is
performed on the solar electricity generation data using
two different approaches: Principal Component Anal-
ysis (PCA) and Under-complete AutoEncoder (UAE).
PCA is a non-parametric technique that learns a linear
transformation of the input data, whereas UAE is a
specific artificial neural network architecture that learns
the parameters of an encoder and a decoder function
minimizing the reconstruction error of the input space.
Then, we interpret the latent variables characterizing
the solar energy generation process by analyzing the
similarities of the different cities with an agglomerative
hierarchical clustering algorithm. This analysis provides
also a comparison between the feature extraction capa-
bilities of PCA and UAE.
The paper is organized as follows. In Section II we
provide a description of the solar irradiance dataset.
In Section III we describe the system model used to
calculate the solar electricity from a PV system; whereas
in Section IV we introduce the features extraction algo-
rithms. Section V discusses the classification performed
by the agglomerative hierarchical clustering algorithm
and gives an interpretation of the extracted features from
the solar energy generation data. Finally, in Section VI
we draw our conclusions.
The solar irradiance dataset considered in this
work [11], contains hourly solar irradiance values,
in W/m2, collected from February 1st, 2004 to
December 31st, 2006. In particular, we consider 67 dif-
ferent cities, located in Europe, Middle East, and North
The solar electricity generation is estimated using the
model described in Section III. In particular, the model
parameters are set considering a Panasonic N235B PV
module, which has single cell efficiencies of about 21%,
delivering about 186 W/m2. Each module is composed
of an array of 16 ×16 solar cells (i.e., a surface of
4.48 m2). We consider this particular dimension since
it represents a realistic size for supplying renewable
powered small base stations [12].
We define D={Dy1,...,DyK}as the dataset
containing the traces of generated energy, with K= 67.
In particular, for each city y, we have a set Dy=
Fig. 1. Source model used to characterize the solar energy generation
{e1,...,en}containing ndaily traces of the generated
energy ei,i∈ {1, . . . , n}. The vector eihas 24
elements, each one containing the amount of energy
generated in the respective hour of the day. Since our
dataset contains 35 months of measurements, n= 1069.
In this section, we describe the source model used
to characterize the electricity generated by a PV panel.
The key building blocks of the model are illustrated in
Fig. 1 and they are the solar source, the photovoltaic
panel and the DC/DC power processor.
A. Astronomical model
We define the effective solar irradiance that hits a
photovoltaic panel as Ieff. This term depends on several
factors, e.g., the inclination of the panel, the geograph-
ical location, the hour of the day and the day of the
In order to translate the solar irradiance, Isun, into
effective solar irradiance, Ieff, we consider the following
astronomical model. According to [13], the effective
solar irradiance, Ieff is proportional to cos Θ, where
Θ[0,90]is the angle between the sunlight and
the normal to the solar module surface. It can be
computed as a function of time tby Ieff (t, N ) =
Isun(t, N ) max(0,cos Θ(t, N )), where Nis the day
number in a year (i.e., N= 1 for January 1st and
N= 365 for December 31st).
The value of cos Θ is calculated as:
cosΘ(t, N ) = sinγ(N)·sin La ·cos β(1)
sin γ(N)·cos La ·sin β·cos α+
+ cos γ(N)·cos La ·cos β·cos ω(t, N )+
+ cos γ(N)·sin La ·sin β·cos α·cos ω(t, N )+
+ cos γ(N)·sin β·sin α·sin ω(t, N )
La [0,90]is the location latitude;
Lo is the location longitude;
γis the declination angle;
ω(t, N )[0,360]is the hour angle;
βis the inclination of the solar panel towards the
sun on the horizon;
αis the azimuthal displacement, which takes values
different from zero if the normal to the plane of
the solar panel is not aligned with the plane of the
corresponding meridian i.e., the solar panel faces
East (α < 0) or West (α > 0).
The declination angle γis due to the elliptic orbit
of the Earth around the sun and the fact the Earth is
tilted on itself at an angle of 23.45. It is defined as the
angular distance North or South of the Earth’s equator
and it can be calculated as:
γ(N)'sin1[sin (23.45) sin (D(N))] (2)
where D(N) = 360 (N81) /365.
The hour angle ωis defined as the azimuth’s angle of
the sun’s rays due to the Earth’s rotation and it can be
calculated as:
ω(t, N ) = 15 (AST (t, N )12)(3)
where AST(t, N )[0,24] hour is the apparent solar
time. We can calculate it as:
AST (t, N ) = t0+ ∆t+ ET(N)(4)
where t0is the local standard time adjusted to account
for the daylight saving time. tis the time displace-
ment between the selected time zone and the time at
the reference Greenwich meridian. It is computed as
t= (LoGMA)/15where GM A =U T Coff ×15
is the Greenwich meridian angle and corresponds to
the angle between the Greenwich meridian and the
meridian of the selected time zone. U T Coff is the time
offset between Greenwich and the time zone whereas
15 is the rotation angle of the Earth per hour. Fi-
nally, ET (N)'[9.87 sin(2D(N))7.53 cos(D(N))
1.5 sin(D(N))]/60 is known as the equation of time.
B. Solar panel model
We consider a solar panel composed of nsc solar cells
connected together. A number npof them are connected
in parallel, whereas nsare connected in series. Thus,
nsc =npns.
The composition of the I-V curves of the solar cells
allows obtaining the I-V curve used to characterize the
solar panel. The I-V curve of a solar cell is given by the
superposition of the current generated by the solar cell
diode in the dark with the current due to the sunlight
hitting the cell, defined as light-generated current il. We
can approximate this curve as:
iout 'iliohexp qv
nkT 1i(5)
where qis the elementary charge, vis the cell voltage,
kis the Boltzmann’s constant, Tis the temperature in
Kelvin degrees, n1is the diode ideal factor. Finally,
iois the dark saturation current and corresponds to the
diode leakage current when there is not light. It depends
on the area and the technology of the solar cell.
We define isc as the short circuit current, which
corresponds to the maximum current for the cell. We
can normalize the effective irradiance, Ieff with respect
to the maximum radiation of 1 kW/m2, obtaining the
radiation rate F(t, N )=0.001·Ieff (t, N). Then, we can
compute the light-generated current for a single solar
cells as il(t, N ) = iscF(t, N )and obtain iout(t, N )for
a single solar cells using equation (5). Finally, the total
current generated by the solar module is iM
out(t, N ) =
npiout(t, N ).
C. Power processor model
Every voltage or current source has a maximum
power point, at which the average power delivered to
its load is maximized. In general, the load of a device
does not match the optimal one, required to extract
the maximum power from the solar source. To solve
this problem, a power processor is used to emulate the
optimal load by adjusting the source voltage until the
power extracted from it is maximized.
In this paper, in order to account for the DC/DC
power processor, we have computed the operating point
out, vM)for which the extracted power P=iM
is maximized.
We have obtained the maximum power PMP as:
PMP =ηmax
out, vM=η·npnsmax
where iout is given by equation (5) and η(0,1) is
the power processor conversion efficiency.
Two different approaches have been used to reduce
the dimensionality of the input data and extract mean-
ingful features: PCA and UAE, described in the follow-
ing sub-sections.
A. Principal Component Analysis (PCA)
PCA is a non-parametric technique for extracting
relevant features from a dataset. The purpose is to reduce
the dimensionality of the dataset by finding a new set of
variables, smaller than the original, that retains most of
the original information. Those new variables are called
principal components (PC). They are uncorrelated and
they are ordered by the fraction of the total information
each retains [14].
Given nobservations of the vector e= (e1, . . . , e24 ),
the first PC is computed as:
where a1= (a1,1, a2,1, . . . , a24,1)is the vector that
maximize the variance of z1.
In a similar way, the kth PC (with k= 1,...,24) is
computed as:
Fig. 2. Autoencoder topology used for extracting features of the solar
energy generation of the 67 cities in the dataset.
where the vector akis chosen such that the variance of
zkis maximum, subject to cov[zk, zl]=0for k > l 1
and aT
kak= 1.
The generic observation eican be written as the sum
of its PCs:
According to the given definitions, it can be easily
proved that the first PC retains the greatest amount of
variation in the sample, whereas the kth PC, zk, retains
the greatest kth fraction of the variation in the sample.
This fact allows us to approximate each observation by
truncating the sum at the first m < 24 PCs:
The number of considered PCs has been set to m= 2
since tests performed on the available data shows that
the first 2 PCs retains the 94% of the information
(respectively the 79% and the 15% for the 1st and the
2nd PC). Thus, we can associate to each observation
eia feature vector hPCA
i= [zi,1, zi,2]. In this way,
for each Dy, we obtain a set of 2-dimensional features
n}, which is a compressed
representation of the evolution of the solar energy gen-
eration in the city y.
B. Under-complete Autoencoder (UAE)
An under-complete autoencoder (UAE) is an artifi-
cial neural network used for unsupervised learning of
representations from a set of data, for the purpose of
dimensionality reduction [15]. It learns to compress data
from the input layer into a short code, and then uncom-
press that code into something that closely matches the
original data (output layer). The set of hidden layers with
decreasing number of neurons till reaching the central
layer is called encoder. Another set of hidden layers
from the central layer to the output layer is for the
reconstruction of the original data and named decoder.
Figure 2 shows the autoencoder used in this work.
The input and output layers are composed of 24 neurons,
Fig. 3. Clusters obtained by using the PCA approach.
each one representing the amount of energy generated
in the corresponding hour of the day. The single hidden
layer, named feature layer, is composed of 2 neurons. In
this way, the information about the 24-hour solar energy
generation trace is coded into a feature of dimension 2.
We consider the Multilayer Perceptron as basic archi-
tecture, consisting of multiple fully connected layers of
neurons. The output of a neuron is computed using the
following equation:
output =f X
where xis the input of the neuron, wis the weight of
the connection to the neuron, bis the bias and fis the
activation function. The neurons of the input and feature
layers use the ReLU as activation function (fa(x) =
max(0, x)), whereas the neurons of the output layer use
the sigmoid function (fb(x)=1/(1 + ex)).
The dataset Dis split into two portions: the training
set T, containing nt= 730 daily traces and the valida-
tion set Vcontaining the remaining nntdaily traces.
The first set is used for training the autoencoder. During
this phase, the backpropagation algorithm iteratively
updates the weights of the connections between neurons
to minimize the reconstruction loss (training loss). At the
same time, the autoencoder is used to reconstruct the
traces contained in the validation set. The validation set
provides an unbiased evaluation of the model fit on the
training set (validation loss). The training and validation
losses decrease with the number of epochs (i.e., training
events) and they both converge to 103in 80 epochs.
After training, for each Dy, the encoder computes a set
of 2-dimensional features Fy
We define the Centroid of the city y as the centroid
of the features of the city y, computed as:
i=1 hPCA,UAE
Fig. 4. Clusters obtained by using the UAE approach.
where the value of the centroid depends on the approach
used to extract the features (i.e., PCA or UAE).
A distance-based clustering named agglomerative hi-
erarchical clustering algorithm [16] is applied to the cen-
troids to group the cities according to their similarities.
The similarity between cities is expressed as the distance
between their centroids.
The number of clusters has been selected by perform-
ing the silhouette analysis [17]. This technique provides
a measure of similarity of an object in its own cluster
compared to other clusters. The silhouette value ranges
in the interval [-1,1], being the highest value the best
match of the object in the cluster and the smallest
the poorest. The configuration is appropriate if most of
the objects have a high value. Otherwise, the cluster
configuration may have too many or too few classes.
We have set the clustering algorithm to find the number
of clusters that maximize the average silhouette value
and we obtained 2 clusters in the case of PCA and 5
clusters in the case of UAE.
The result of the clustering based on PCA feature
centroids is shown in Fig. 3, whereas the map obtained
using UAE is reported in Fig. 4. Moreover, we show
the average hourly solar energy generated for each
cluster obtained with PCA and UAE in Fig. 5 and
Fig. 6, respectively. The analysis of such variable for
each cluster drives our interpretation of the extracted
features to a more understandable model. In fact, we
can distinguish four different parameters characterizing
each identified cluster, namely: average daily generated
energy EH, solar energy peak value Pv, solar energy
peak hour Phand average daylight time D. The values
of those parameters are reported in Table I and Table II
for PCA and UAE, respectively.
The map obtained with PCA is divided into two
clusters (Fig. 3). The first cluster covers the North-East
area and is characterized by a low amount of energy
generation (lower Ehand Pv). The second cluster covers
the South-West area and is characterized by higher solar
Cluster Ph[hr] Pv[kWh] Eh[kWh] D[hrs]
1 11 0.4 2.1 12
2 12 0.6 3.2 12
Cluster Ph[hr] Pv[kWh] Eh[kWh] D[hrs]
1 12 0.6 3.1 12
2 11 0.3 1.8 13
3 11 0.5 2.5 11
4 12 0.3 1.9 13
5 10 0.6 2.9 11
energy generation. Note that the two clusters differ also
for the peak hours Ph. On the other hand, from the
map obtained by UAE (Fig. 4), we see that the North of
Europe is divided into two parts (clusters 2 and 4). Those
clusters have the same values of Pv,D, similar Eh, but
they differ in terms of solar peak hour Ph. The south
area is divided into three clusters (1, 3 and 5). Those
clusters are very similar in terms of peak energy value
Ph, but they differ for peak hour Ph, average energy Eh
and daylight hours D.
Note that the parameter Ehrepresents the variable
used by common solar maps to discriminate the different
locations. In fact, those maps are usually based on the
average or annual amount of solar energy generation.
Therefore, PCA and UAE are able to extract two and
three new variables featuring the solar energy generation
process, respectively. The difference on the number of
extracted latent variables may be due to the non-linear
activation functions of the autoencoder, which obtains a
better projection of the input data into the feature space
compared to the linear PCA.
The geographical representation described in this pa-
per is based on the characterization of several different
temporal behaviors of the solar energy generation in
diverse geographical locations. In particular, we believe
that it may be helpful in designing energy management
systems that have to control industrial processes, which
are strictly related to the human activity. In fact, the
parameters identified by our analysis provide a direct
relation between the solar energy generation process and
the time of the day.
In this paper, we have proposed an unsupervised
method to learn hidden features of the solar energy
generation from a PV system that may give a more accu-
rate characterization of the process. In a first step, solar
radiation data has been converted into instantaneous
solar power through a detailed source model. Then,
Fig. 5. Average hourly solar energy generation of the clusters obtained by using the PCA approach. The shaded area represents the standard
deviation with respect to the other cities in the cluster.
Fig. 6. Average hourly solar energy generation of the clusters obtained by using the UAE approach. The shaded area represents the standard
deviation with respect to the other cities in the cluster.
two different approaches, namely PCA and autoencoder,
have been used to extract meaningful features from the
traces of the solar energy generation in an unsupervised
manner. A hierarchical clustering algorithm has been
used to group the locations according to their similar-
ities in terms of solar energy generation. The results
show that clustering on the extracted features provides
support for learning latent variables of the solar energy
generation process that may be used for a more detailed
characterization of different geographical locations.
This work has received funding from the Euro-
pean Union Horizon 2020 research and innovation
programme under the Marie Sklodowska-Curie grant
agreement No 675891 (SCAVENGE) and by the Span-
ish Government under project TEC2017-88373-R (5G-
[1] M. P. Mills, “The cloud begins with coal: Big data, big networks,
big infrastructure, and big power,Digital Power Group, August
[2] A. S. Andrae and T. Edler, “On global electricity usage of
communication technology: trends to 2030,” MDPI Challenges,
vol. 6, no. 1, pp. 117–157, April 2015.
[3] T. Han and N. Ansari, “Powering mobile networks with green
energy,IEEE Wireless Communications, vol. 21, no. 1, pp. 90–
96, February 2014.
[4] G. Piro, M. Miozzo, G. Forte, N. Baldo, L. A. Grieco, G. Boggia,
and P. Dini, “HetNets Powered by Renewable Energy Sources:
Sustainable Next-Generation Cellular Networks,” IEEE Internet
Computing, vol. 17, no. 1, pp. 32–39, January 2013.
[5] N. Piovesan, A. F. Gambin, M. Miozzo, M. Rossi, and P. Dini,
“Energy sustainable paradigms and methods for future mobile
networks: A survey,” Computer Communications, vol. 119, pp.
101 – 117, 2018.
[6] M. Miozzo, D. Zordan, P. Dini, and M. Rossi, “Solarstat: Mod-
eling photovoltaic sources through stochastic markov processes,
in Energy Conference (ENERGYCON), 2014 IEEE International.
IEEE, 2014, pp. 688–695.
[7] M. ri, T. A. Huld, E. D. Dunlop, and H. A. Ossenbrink, “Potential
of solar electricity generation in the european union member
states and candidate countries,” Solar Energy, vol. 81, no. 10,
pp. 1295 – 1305, 2007.
[8] Y. Bengio, A. Courville, and P. Vincent, “Representation learn-
ing: A review and new perspectives,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp.
1798–1828, Aug 2013.
[9] M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and
B. Schuller, “audeep: Unsupervised learning of representations
from audio with deep recurrent neural networks,” arXiv preprint
arXiv:1712.04382, 2017.
[10] A. K. Yadav and S. Chandel, “Solar radiation prediction using
artificial neural network techniques: A review,” Renewable and
Sustainable Energy Reviews, vol. 33, pp. 772 – 781, 2014.
[11] SoDa. Solar radiation data. [Online]. Available: http://www.soda-
[12] N. Piovesan and P. Dini, “Optimal direct load control of renew-
able powered small cells: A shortest path approach,” Internet
Technology Letters, vol. 1, no. 1, p. e7.
[13] J. V. Dave, P. Halpern, and H. J. Myers, “Computation of inci-
dent solar energy,IBM Journal of Research and Development,
vol. 19, no. 6, pp. 539–549, Nov 1975.
[14] I. T. Jolliffe, Principal components in regression analysis.
Springer, 2002.
[15] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning.
MIT Press, 2016.
[16] B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Hierarchical
Clustering. Wiley-Blackwell, 2011, ch. 4, pp. 71–110.
[17] P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation
and validation of cluster analysis,” Journal of computational and
applied mathematics, vol. 20, pp. 53–65, 1987.
... Hence, it is always measured in watts per square metre (W /m 2 ). According to [11] and [12], the total power that can be harvested by a solar panel at any instantaneous time is dependent on the irradiance. In this paper, we define the total irradiance hitting R H on an arbitrarily oriented solar panel mounted on top of a BLE beacon based on the well-developed model by [13]. ...
... The database contains hourly information about solar radiation and other meteorological data. The solar radiation has been converted into harvested solar energy by considering the model introduced in [22]. ...
Full-text available
The deployment of dense networks of small base stations represents one of the most promising solutions for future mobile networks to meet the foreseen increasing traffic demands. However, such an infrastructure consumes a considerable amount of energy, which, in turn, may represent an issue for the environment and the operational expenses of the mobile operators. The use of renewable energy to supply the small base stations has been recently considered as a mean to reduce the energy footprint of the mobile networks. In this paper, we consider a hierarchical structure in which part of the base stations are powered exclusively by solar panels and batteries. Base stations are grouped in clusters and connected in a micro-grid. A central controller enables base station sleep mode and energy sharing among the base stations based on the available energy budget and the traffic demands. We propose three different implementations of the controller through Machine Learning models, namely Imitation Learning, Q-Learning and Deep Q-Learning, capable of learning optimal sleep mode and energy sharing policies. We provide an exhaustive discussion on the achieved performance, complexity and feasibility of the proposed models together with the energy and cost savings attained.
In this chapter, we describe the design of controlling schemes for energy self-sustainable mobile networks through Deep Learning. The goal is to enable an intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energies. To achieve this goal, we formulate an on-line grid energy and network throughput optimization problem considering both centralized and distributed Deep Reinforcement Learning implementations. We provide an exhaustive discussion on the reference scenario, the techniques adopted, the achieved performance, the complexity and the feasibility of the proposed models, together with the energy and cost savings attained. Results demonstrate that Deep Q-Learning based algorithms represent a viable and economically convenient solution for enabling energy self-sustainability of mobile networks grouped in micro-grids.
Full-text available
In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.
Full-text available
In this letter, we propose an optimal direct load control of renewable powered small base stations (SBSs) in a two-tier mobile network based on dynamic programming (DP). We represent the DP optimization using Graph Theory and state the problem as a Shortest Path search. We use the Label Correcting Method to explore the graph and find the optimal ON/OFF policy for the SBSs. Simulation results demonstrate that the proposed algorithm is able to adapt to the varying conditions of the environment, namely renewable energy arrivals and traffic demands. The key benefit of our study is that it allows to elaborate on the behavior and performance bounds of the system and gives a guidance for approximated policy search methods.
Full-text available
This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.
Full-text available
Explosive mobile data demands are driving a significant growth in energy consumption in mobile networks, and consequently a surge of carbon footprints. Reducing carbon footprints is crucial in alleviating the direct impact of greenhouse gases on the earth environment and the climate change. With advances of green energy technologies, future mobile networks are expected to be powered by green energy to reduce their carbon footprints. This article provides an overview on the design and optimization of green energy enabled mobile networks, discusses the energy models for the analysis and optimization of the networks, and lays out basic design principles and research challenges on optimizing the green energy powered mobile networks.
Full-text available
Renewable energy could be the key for sustainable next-generation cellular networks. The authors' approach would let mobile operators feed base stations in a heterogeneous network using renewable energy sources. The authors compare their method to a classical grid-powered solution. They evaluate costs and CO2 emissions savings for different scenarios to demonstrate that properly powering a heterogeneous network with renewable energy can be a sustainable and economically convenient solution.
Full-text available
In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices. The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab(TM) and is available under the GPL license at[1].
Full-text available
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
Summary This document is part of Subvolume B ‘Physical and Chemical Properties of the Air’ of Volume 4 ‘Meteorology’ of Landolt-Börnstein - Group V Geophysics.
As illustrated in the other chapters of this book, research continues into a wide variety of methods of using PCA in analysing various types of data. However, in no area has this research been more active in recent years, than in investigating approaches to regression analysis which use PCs in some form or another.
Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.