4 research outputs found
Energy-aware task allocation for energy harvesting sensor networks
10.1186/s13638-015-0490-3Eurasip Journal on Wireless Communications and Networking201611-1
Analysis of time series forecasting in application to solar energy harvest
The promised future applications in solar energy harvest have been remarkably recognized. However, the hourly forecasting of normal solar irradiance (NSI) outputs is considered a problem due to the dynamic nature of meteorological information not only in a day but also across days. The thesis proposed three neural network models including a dense layer without a hidden layer (DNN_h0), a dense neural network with two hidden layers (DNN_h2), a dense neural network with two hidden layers associated with one intermediate metrological feature (air temperature: T) (DNN_h2T), and dense neural network with two hidden layers associated with 7 intermediate metrological features (DNN_h2F). These models would be used to forecast an hourly prediction of normal solar irradiance (NSI) across an entire day. As well as, we proposed two configurations to represent our datasets: FTC (sine-cosine) and 1H (one-hot) encodings. In addition, we used metrological features such as air temperature T and others to determine the effectiveness of a model’s performance in terms of mean absolute error (MAE). We conducted two groups of experiments: single-step and multi-step prediction models by using one real-world dataset (NREL). As a result, the comparison is revealed that the (NSI) has an acceptable model performance in both FTC and 1H encodings for the multi-step models by using an intermediate metrological feature: air temperature T in the (DNN_h2T) model. Whereas the single-step model (DNN_h0) has shown slightly acceptance to find a well performance to predict the (NSI), while the (DNN_h2) model shows a significant (MAE) values in both encodings
MODELING, METHODOLOGY AND APPLICATIONS FOR RESOURCE MANAGEMENT IN ENERGY HARVESTING SYSTEMS.
Ph.DDOCTOR OF PHILOSOPH
Energy neutral solar powered wireless sensor nodes
Baterijski napajani senzorski čvorovi omogućavaju prikupljanje podataka sa senzora koji su postavljeni na teško dostupnim ili udaljenim mestima. Ograničenje upotrebe baterijski napajanih senzorskih
čvorova predstavlja konačan kapacitet baterije. U cilju produženja vremena rada do trenutka pražnjenja baterije koriste se algoritmi za optimizaciju potrošnje i perfomansi. Algoritam na osnovu zadatog
kriterijuma i primenom tehnika za smanjenje potrošnje omogućava postizanje kompromisa između
performansi i potrošnje, a samim tim i vremena trajanja baterije. Međutim, koliko god da je algoritam
za optimizaciju efikasan, baterija će se eventualno isprazniti.
Prikupljanje energije iz okoline predstavlja potencijalno rešenje problema konačnog kapaciteta
baterije. Ukoliko se koristi prikupljanje energije iz okoline, cilj algoritma optimizacije menja se u
maksimizaciju performansi tako da se ne ugrozi operativnost senzorskog čvora. Ovaj koncept se u
literaturi naziva koncept energetske neutralnosti. Da bi se postigla energetska neutralnost, algoritam
pored informacije o trenutnom stanju napunjenosti baterije treba da ima i predviđanje o energiji koja
će moći da se prikupi u budućnosti.
U okviru ove disertacije prvo su analizirane postojeće tehnike za smanjenje potrošnje. Prikazane
su tehnike koje se primenjuju prilikom razvoja i tehnike koje se primenjuju u toku rada sistema.
Nedostatak postojećih tehnika za smanjenje potrošnje u toku rada je što im je primena ograničena
isključivo na opseg gde ne dolazi do degradacije performansi. To je posledica nepostojanja objektivne
metrike degradacije.
Predložena je nova objektivna metrika degradacije performansi sistema za rad u realnom vremenu.
Metrika degradacije performansi omogućava da se funkcija degradacije pridruži svakom tasku na sistemu prema vremenskoj kritičnosti. Uvođenjem metrike degradacije omogućeno je proširenje opsega
primene postojećih tehnika za smanjenje potrošnje. Na osnovu uvedene metrike degradacije predstavljena je i nova tehnika za smanjenje potrošnje koja se zasniva na kontroli utilizacije (iskorišćenja
procesorskog vremena). Predložena tehnika proširuje polje sistemskih parametara, čime je omogućen
fin odabir kompromisa između potrošnje i performansi. Tehnika je verifikovana u simuliranom okru-
ženju, gde je za proizvoljno izabran skup taskova prikazano da predložena tehnika omogućava odabir
rada sistema sa manjom degradacijom performansi i manjom potrošnjom u odnosu na standardne
tehnike za smanjenje potrošnje.
Nakon toga su analizirane postojeće tehnike za predviđanje dostupne energije u budućnosti. U
zavisnosti od toga kako predviđaju dostupnu energiju za naredne vremenske intervale, tehnike mogu
da se podele na tehnike koje koriste podatke iz prethodnih vremenskih intervala i tehnike koje koriste
podatke iz vremenske prognoze. Tehnike koje koriste podatke iz prethodnih vremenskih intervala
su jednostavnije za implementaciju jer su podaci uvek dostupni i mogu da daju bolje rezultate za
predviđanje za naredni vremenski interval, odnosno kratkoročnu vremensku prognozu. Tehnike koje
su zasnovane na vremenskoj prognozi uglavnom koriste podatke o oblačnosti, kojima modulišu podatke
o solarnom zračenju za potpuno vedar dan. Nedostatak ovih tehnika je što je potrebno nabaviti podatke
o vremenskoj prognozi, što predstavlja dodatni utrošak energije. Prednosti su što pružaju znatno manju
grešku predviđanja za više sati u budućnosti, odnosno za srednjeročno i dugoročno predviđanje...Battery powered wireless sensor nodes enable data collection from sensors placed in inaccessible
or remote locations. The use of battery powered wireless sensor nodes is limited by finite battery
capacity. In order to extend operation time before battery is depleted, power and performance
management algorithms are used. Based on defined optimization criteria and using consumption
reduction techniques, the algorithm enables trade-off between performance and power, thus extending
battery life. However, regardless of how effective management algorithm is, the battery will be
depleted eventually.
Energy harvesting is a possible solution to finite battery capacity issue. When energy harvesting
is used, the optimization goal switches towards maximizing performance while maintaining sensor
node operational. This concept is referred to as the concept of energy neutrality. In order to achieve
energy neutrality, the algorithm needs information about current battery state of charge, as well as the
prediction of future available harvested energy.
In this dissertation, first the existing techniques for consumption reduction have been analyzed.
Techniques have been separated into techniques used during design and techniques used during runtime. The downside of existing techniques for consumption reduction at run-time is that their use is
limited exclusively to the domain where no performance degradation occurs. This is the consequence
of lack of objective performance degradation metrics.
A new objective performance degradation metrics for real-time systems has been introduced. Performance degradation metrics are used to assign degradation function to each task in the system based
on consequence of missing a deadline. The introduction of degradation metrics enables extension of
domain of application for existing consumption reduction techniques. Based on introduced degradation metrics a new consumption reduction technique has been introduced. Introduced technique
extends operating performance point parameters, thus enabling fine trade-off between power and performance. The technique has been verified in simulated environment using a selected set of tasks,
where it has been shown that it can enable system operation with less performance degradation, less
power consumption, or both, compared to standard consumption reduction techniques.
Next, existing techniques for prediction of future available energy have been analyzed. Depending
on how prediction for the following time slots is obtained, techniques can be separated into techniques
that use measurements from previous time slots and techniques that use weather forecast data. Techniques that use measurements from previous time slots have simpler implementation since previous
measurements are already available and can provide better prediction results for following time slots,
i.e. short-term prediction. Techniques that use weather forecast data usually use the cloud cover data,
which is used to modulate the clear-sky solar radiation data. The downside of these techniques is that
weather forecast data needs to be obtained somehow, which incurs additional power consumption.
The advantage of these techniques is that they can provide less prediction error when predicting for
time slots further in the future, i.e. the medium-term and long-term prediction..