scholarly journals Efficient Wind Speed Forecasting for Resource-Constrained Sensor Devices

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 983
Author(s):  
Sergio Herrería-Alonso ◽  
Andrés Suárez-González ◽  
Miguel Rodríguez-Pérez ◽  
Raúl F. Rodríguez-Rubio ◽  
Cándido López-García
Keyword(s):  
Wind Speed ◽  
Energy Harvesting ◽  
Wind Energy ◽  
Arima Model ◽  
Real Data ◽  
Low Complexity ◽  
Sensor Nodes ◽  
Data Sets ◽  
Resource Constrained ◽  
Power Sources

Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the dynamics of energy harvesting. On the other hand, resource-constrained devices with limited hardware capacities (such as sensor nodes) must resort to forecasting schemes of low complexity for their predictions in order to avoid squandering their scarce power and computing capabilities. In this paper, we present a new efficient ARIMA-based forecasting model for predicting wind speed at short-term horizons. The performance results obtained using real data sets show that the proposed ARIMA model can be an excellent choice for wind-powered sensor nodes due to its potential for achieving accurate enough predictions with very low computational burden and memory overhead. In addition, it is very simple to setup, since it can dynamically adapt to varying wind conditions and locations without requiring any particular reconfiguration or previous data training phase for each different scenario.

scholarly journals An Optimized Flutter-Driven Triboelectric Nanogenerator with a Low Cut-In Wind Speed

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 366
Author(s):  
Yang Xia ◽  
Yun Tian ◽  
Lanbin Zhang ◽  
Zhihao Ma ◽  
Huliang Dai ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Energy Harvesting ◽  
Wind Energy ◽  
Output Power ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
Triboelectric Nanogenerator ◽  
Vibration Behavior ◽  
Air Speed

We present an optimized flutter-driven triboelectric nanogenerator (TENG) for wind energy harvesting. The vibration and power generation characteristics of this TENG are investigated in detail, and a low cut-in wind speed of 3.4 m/s is achieved. It is found that the air speed, the thickness and length of the membrane, and the distance between the electrode plates mainly determine the PTFE membrane’s vibration behavior and the performance of TENG. With the optimized value of the thickness and length of the membrane and the distance of the electrode plates, the peak open-circuit voltage and output power of TENG reach 297 V and 0.46 mW at a wind speed of 10 m/s. The energy generated by TENG can directly light up dozens of LEDs and keep a digital watch running continuously by charging a capacitor of 100 μF at a wind speed of 8 m/s.

Design and performance evaluation of vertical axis wind turbine for wind energy harvesting at railway

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hashwini Lalchand Thadani ◽  
Fadia Dyni Zaaba ◽  
Muhammad Raimi Mohammad Shahrizal ◽  
Arjun Singh Jaj A. Jaspal Singh Jaj ◽  
Yun Ii Go
Keyword(s):  
Wind Speed ◽  
Energy Harvesting ◽  
Wind Energy ◽  
Wind Turbine ◽  
High Speed ◽  
Vertical Axis ◽  
Speed Ratio ◽  
Vertical Axis Wind Turbine ◽  
Modeling Tools ◽  
Content Type

PurposeThis paper aims to design an optimum vertical axis wind turbine (VAWT) and assess its techno-economic performance for wind energy harvesting at high-speed railway in Malaysia.Design/methodology/approachThis project adopted AutoCAD and ANSYS modeling tools to design and optimize the blade of the turbine. The site selected has a railway of 30 km with six stops. The vertical turbines are placed 1 m apart from each other considering the optimum tip speed ratio. The power produced and net present value had been analyzed to evaluate its techno-economic viability.FindingsComputational fluid dynamics (CFD) analysis of National Advisory Committee for Aeronautics (NACA) 0020 blade has been carried out. For a turbine with wind speed of 50 m/s and swept area of 8 m2, the power generated is 245 kW. For eight trains that operate for 19 h/day with an interval of 30 min in nonpeak hours and 15 min in peak hours, total energy generated is 66 MWh/day. The average cost saved by the train stations is RM 16.7 mil/year with battery charging capacity of 12 h/day.Originality/valueWind energy harvesting is not commonly used in Malaysia due to its low wind speed ranging from 1.5 to 4.5 m/s. Conventional wind turbine requires a minimum cut-in wind speed of 11 m/s to overcome the inertia and starts generating power. Hence, this paper proposes an optimum design of VAWT to harvest an unconventional untapped wind sources from railway. The research finding complements the alternate energy harvesting technologies which can serve as reference for countries which experienced similar geographic constraints.

scholarly journals A New Strategy of Hybrid Models using ARIMA, ANN, and DWT in Time Series Modelling

2021 ◽  
Vol 25 (1) ◽  
pp. 27-50
Author(s):  
Tsung-Lin Li ◽  
◽  
Chen-An Tsai ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Real Data ◽  
Hybrid Models ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Model Combining ◽  
Time Series Modelling ◽  
Significant Difference

Time series forecasting is a challenging task of interest in many disciplines. A variety of techniques have been developed to deal with the problem through a combination of different disciplines. Although various researches have proved successful for hybrid models, none of them carried out the comparisons with solid statistical test. This paper proposes a new stepwise model determination method for artificial neural network (ANN) and a novel hybrid model combining autoregressive integrated moving average (ARIMA) model, ANN and discrete wavelet transformation (DWT). Simulation studies are conducted to compare the performance of different models, including ARIMA, ANN, ARIMA-ANN, DWT-ARIMA-ANN and the proposed method, ARIMA-DWT-ANN. Also, two real data sets, Lynx data and cabbage data, are used to demonstrate the applications. Our proposed method, ARIMA-DWT-ANN, outperforms other methods in both simulated datasets and Lynx data, while ANN shows a better performance in the cabbage data. We conducted a two-way ANOVA test to compare the performances of methods. The results showed a significant difference between methods. As a brief conclusion, it is suggested to try on ANN and ARIMA-DWT-ANN due to their robustness and high accuracy. Since the performance of hybrid models may vary across data sets based on their ARIMA alike or ANN alike natures, they should all be considered when encountering a new data to reach an optimal performance.

Piezoelectric wind energy harvester for low-power sensors

2011 ◽  
Vol 22 (18) ◽  
pp. 2215-2228 ◽  
Author(s):  
Jayant Sirohi ◽  
Rohan Mahadik
Keyword(s):  
Energy Harvesting ◽  
Wind Energy ◽  
Mechanical Energy ◽  
Electrical Energy ◽  
Load Resistance ◽  
Operating Conditions ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Piezoelectric Energy

There has been increasing interest in wireless sensor networks for a variety of outdoor applications including structural health monitoring and environmental monitoring. Replacement of batteries that power the nodes in these networks is maintenance intensive. A wind energy–harvesting device is proposed as an alternate power source for these wireless sensor nodes. The device is based on the galloping of a bar with triangular cross section attached to a cantilever beam. Piezoelectric sheets bonded to the beam convert the mechanical energy into electrical energy. A prototype device of size approximately 160 × 250 mm was fabricated and tested over a range of operating conditions in a wind tunnel, and the power dissipated across a load resistance was measured. A maximum power output of 53 mW was measured at a wind velocity of 11.6 mph. An analytical model incorporating the coupled electromechanical behavior of the piezoelectric sheets and quasi-steady aerodynamics was developed. The model showed good correlation with measurements, and it was concluded that a refined aerodynamic model may need to include apparent mass effects for more accurate predictions. The galloping piezoelectric energy-harvesting device has been shown to be a viable option for powering wireless sensor nodes in outdoor applications.

scholarly journals Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4217
Author(s):  
Diego Martín ◽  
Damaris Fuentes-Lorenzo ◽  
Borja Bordel ◽  
Ramón Alcarria
Keyword(s):  
Real World ◽  
Prediction Models ◽  
Smart Cities ◽  
Arima Model ◽  
Low Complexity ◽  
Sensor Nodes ◽  
Statistical Prediction ◽  
Computational Time ◽  
Future Research ◽  
Ambient Intelligent

Sensor networks in real-world environments, such as smart cities or ambient intelligent platforms, provide applications with large and heterogeneous sets of data streams. Outliers—observations that do not conform to an expected behavior—has then turned into a crucial task to establish and maintain secure and reliable databases in this kind of platforms. However, the procedures to obtain accurate models for erratic observations have to operate with low complexity in terms of storage and computational time, in order to attend the limited processing and storage capabilities of the sensor nodes in these environments. In this work, we analyze three binary classifiers based on three statistical prediction models—ARIMA (Auto-Regressive Integrated Moving Average), GAM (Generalized Additive Model), and LOESS (LOcal RegrESSion)—for outlier detection with low memory consumption and computational time rates. As a result, we provide (1) the best classifier and settings to detect outliers, based on the ARIMA model, and (2) two real-world classified datasets as ground truths for future research.

scholarly journals Wind power plant forecasting and power prediction methods using Machine Learning Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 687-691
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Power Prediction ◽  
Wind Speed Data

A significant and eligible source such as wind energy has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, wind power forecasting was performed based on daily wind speed data using machine learning algorithms. The proposed method is based on machine learning algorithms to forecast wind power values efficiently. Tests were conducted on data sets to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, several machine learning models were built for analysis on the accuracy level of the respective models, i.e, the accuracy levels of the machine.

Sign in / Sign up

Export Citation Format

Share Document