M.TECH THESIS DEFENCE
OFFSHORE WIND SPEED PREDICTION USING DEEP LEARNING MODELS
Under the supervision of
Prof. Pradip Swarnakar
DEPARTMENT OF SUSTAINABLE ENERGY ENGINEERING
INDIAN INSTITUTE OF TECHNOLOGY (IIT) KANPUR, INDIA
1
Contents
Introduction
Methodology
Results
Conclusions
References
2
Global Scenario for
Renewable Energy
Installed Capacity (2024):
• Total: ~3500 GW (~30% of global electricity).
• Solar PV: 1,200 GW | Wind: 950 GW | Hydropower: 1,200 GW.
Top Players:
• China: Leader in solar, wind, and hydropower.
• USA: Rapid growth in wind, solar, and offshore wind.
• EU: Aggressive Green Deal targets; offshore wind focus.
• India: Fast-growing solar and wind capacity.
3
Global Scenario for
Renewable Energy
Investments:
• $500B+ (2024) focusing on solar PV, wind, and green hydrogen.
Future Outlook:
• Renewables to contribute 70–80% of electricity by 2050.
Challenges:
• Grid integration, storage scalability, supply chain bottlenecks, and policy
gaps.
4
Indian Scenario for
Renewable Energy
Installed Capacity:
• Total renewable energy capacity: Over 125 GW (as of early 2025).
• Solar energy:
~70 GW.
• Wind energy:
~45 GW.
• Biomass energy:
~10 GW.
• Small hydro:
~5 GW.
Global Standing:
• India ranks 4th globally in wind power and 5th in solar energy.
• Among the top renewable energy-producing countries worldwide.
5
Indian Scenario for
Renewable Energy
Future Prospects:
1. Target 2030:
• Achieve 500 GW of non-fossil fuel energy capacity.
• Generate 50% of electricity from renewables.
2. Solar Energy Growth:
• Focus on large-scale solar parks and floating solar plants.
• Expansion of rooftop solar programs to reach 40 GW target.
3. Wind Energy Opportunities:
• Offshore wind projects: India’s first offshore wind farm is
being planned in Gujarat and then in Tamil Nadu.
6
Wind Energy
India is one of the global leaders in wind energy, leveraging its
vast coastline and windy regions to meet renewable energy goals.
Installed Capacity:
• 45 GW of installed onshore wind capacity (as of 2024), making
India the 4th largest wind energy producer globally.
• Major contributing states: Tamil Nadu, Gujarat, Maharashtra,
Karnataka, and Rajasthan.
Wind Resource Potential:
• Estimated potential of 302 GW at 100m hub height, primarily
concentrated in the southern and western states.
7
Wind Energy
Future Outlook:
1. 2030 Target:
• Achieve 140 GW of wind energy capacity (onshore
and offshore combined).
• Offshore wind to contribute 30 GW.
2. Technology and Investment:
• Increasing use of larger wind turbines and AI for
predictive maintenance.
• Investment from domestic and international players to
scale up offshore projects.
3. Integration with Hydrogen Economy:
• Wind energy to play a key role in producing green
hydrogen, especially from offshore installations.
8
Offshore Wind Energy
Emerging Sector:
• India's offshore wind potential is estimated at
70 GW along the coasts of Gujarat and Tamil
Nadu with vast coastline of about 7600 kms.
Key Projects:
• The First Offshore Wind Project (planned in
Gujarat) will have a 1 GW capacity, under
development by the Ministry of New and
Renewable Energy (MNRE).
This Photo by Unknown Author is licensed under CC BY-SA
9
Offshore Wind Energy
Policy and Initiatives:
•
National Offshore Wind Energy Policy (2015): Provides
guidelines for offshore wind development.
•
Collaborations with global entities like the International
Renewable Energy Agency (IRENA) and European
countries.
Challenges:
This Photo by Unknown Author is licensed under CC BY-SA
• High upfront costs and need for advanced technologies.
• Lack of infrastructure for offshore grid connectivity.
10
Why Offshore?
Higher Wind Speeds:
•Offshore wind speeds are generally stronger and more
consistent compared to onshore locations, resulting in higher
energy generation potential.
Large Resource Potential:
•Offshore wind offers immense untapped potential,
particularly in India, with an estimated capacity of 70 GW
within shallow coastal waters.
Reduced Land Constraints:
•Offshore wind projects avoid the challenges of land
acquisition and competing land use that are common with
onshore wind farms.
Proximity to High Demand Centers:
•Coastal areas often have high population density and energy
demand. Offshore wind farms can supply power directly to
these regions, reducing transmission losses.
11
Why Offshore?
Less Visual and Noise Impact:
• Offshore installations are far from populated areas,
minimizing aesthetic and noise concerns that often arise
with onshore wind farms.
Scalability:
• Offshore projects allow for larger turbines and expansive
arrays, which are not feasible onshore due to space and
logistical constraints.
Support for Energy Transition Goals:
• Offshore wind aligns with global and national renewable
energy targets, playing a critical role in reducing reliance
on fossil fuels and achieving net-zero goals.
12
Facilitating Offshore Wind in India (FOWIND)
• It is a project aimed at advancing India's offshore wind energy sector.
• This project is funded by the European Union and implemented by a
consortium led by the Global Wind Energy Council (GWEC), in
collaboration with the Center for Study of Science, Technology and
Policy (CSTEP), the World Institute of Sustainable Energy (WISE), DNV
GL, and other partners.
13
Facilitating Offshore Wind in India (FOWIND)
Key Objectives of FOWIND:
1. Capacity Building: Enhancing the technical and institutional capabilities of stakeholders to plan and implement offshore wind projects.
2. Mapping Offshore Wind Resources: Conducting feasibility studies and wind resource assessments to identify potential offshore wind energy
sites.
3. Policy and Regulatory Frameworks: Providing recommendations to support the development of policies and regulations for offshore wind
energy.
4. Industry Engagement: Facilitating dialogue and collaboration between Indian and international industry players to share expertise and best
practices.
5. Roadmaps and Reports: Publishing roadmaps and technical reports that outline the steps needed to achieve India's offshore wind energy
goals.
14
Facilitating Offshore Wind in India (FOWIND)
FOWIND identified states are Gujarat and Tamil Nadu.
Specifically the sites are near the:
• Gulf of Khambhat, off the coast of Gujarat
• Gulf of Mannar, off the coast of Tamil Nadu
Source: https://gwec.net/members-area-market-intelligence/fowind/
Figure. 1.
15
Facilitating Offshore Wind in India (FOWIND)
• This figure is among the zones identified by the First
Offshore Wind (FOWIND) initiative.
• This particular site, chosen for its potential in wind
energy generation, is situated in the Gulf of Khambhat,
near the Gujarat coast shown in this figure.
• It is around 23 to 40 km seaward from the Pipavav port,
showcasing its significant distance into the marine
territory off the Gujarat Coast.
•
Accessibility to the site is primarily through Pipavav and
Jaffrabad ports, facilitating the logistical aspect of wind
energy exploration.
Source: https://gwec.net/members-area-market-intelligence/fowind/
Figure. 2.
16
Facilitating Offshore Wind in India (FOWIND)
The LiDAR sensor for offshore wind measurement in Tamil
Nadu is located at:
•Vembar, Gulf of Mannar, Tuticorin District, Tamil Nadu
• Coordinates: Approximately 8°46' N latitude and
78°14' E longitude.
This location was chosen based on its proximity to strong and
consistent wind flows, shallow waters suitable for foundation
installation, and strategic alignment with the proposed offshore
wind energy zones.
Source: https://gwec.net/members-area-market-intelligence/fowind/
Figure. 3
17
1. To Develop Accurate Wind Speed Forecasting Models:
Implement advanced machine learning models, such as LSTM, Bi-LSTM,
ConvLSTM, CNN-LSTM, GRU, and CNN, to accurately forecast offshore wind
speeds for different hub heights from 80m to 200m.
2. To Reduce Forecasting Errors:
Employ robust data preprocessing and feature engineering techniques to
minimize uncertainty and variability in wind speed predictions.
3. To Evaluate Model Performance:
Compare the performance of various deep learning techniques based on
key metrics like R² and Mean Absolute Error (MAE) to determine the most
effective approach.
Objectives of the
study?
4. To Analyze Seasonal and Temporal Patterns:
To improve the accuracy of forecasting models, find and include seasonal
and temporal trends in wind speed data.
5. To Enhance Data Predictability:
By implementing advanced forecasting models and statistical validation
techniques, ensuring reliable and accurate insights for decision-making.
6. To Provide Data-Driven Policy Support:
Offer actionable recommendations for energy policy development and
infrastructure planning based on accurate wind speed forecasts.
18
Methodology
Figure. 4.
19
Data Collection
• The data collection step for this thesis on offshore wind speed
prediction is anchored in the procurement of particular and
relevant datasets from the National Institute of Wind Energy
(NIWE) website.
• The LiDAR (Light Detection And Ranging) sensors are crucial for
gathering accurate wind speed data, which is necessary for the
development of offshore wind farms.
• The dataset utilized encompasses the latest available LiDAR
data, spanning from December 2017 to November 2019, thus
providing two years worth of wind speed and other
meteorological measurements crucial for this research.
Offshore Wind LiDAR sensor data for Gujarat coast
20
Data Description
• Total Data Points: 1,05,119
• Non-Null Values: 80,132
• Null Values: 105119-80132=24,987
• Time Interval: Data is recorded every 10 minutes, making
it a time-series dataset.
21
Handling Missing Data with KNN
•KNN Imputation Technique: The missing values were filled using the K-Nearest Neighbors (KNN) algorithm, which
identifies the closest neighboring data points based on feature similarity and computes imputed values accordingly.
•Feature Dependence: KNN imputation leverages correlations among features in the dataset, ensuring the imputed values
align with the underlying patterns and trends.
•Improved Data Integrity: By preserving relationships within the data, KNN imputation minimizes the risk of bias and
supports robust analysis in the subsequent modeling and forecasting steps.
22
Handling Missing Data with KNN
Data with imputation
Data without imputation
Figure. 5.
23
Data Analysis
Observed Data
Figure. 6.
24
Data Analysis
Observed Data
Figure. 7.
25
Data Analysis
Average wind speed for different hub heights
Figure. 8.
Maximum wind speed for different hub heights
Figure. 9.
26
Data Analysis
Seasonal Decomposition
Isolate Seasonal Patterns: It helps to separate the seasonal component from the trend and residuals, making it
easier to analyze and understand periodic fluctuations in the data. This isolation allows for clearer insights into how
certain events or cycles influence the time series.
Improve Model Accuracy: By decomposing a time series into its trend, seasonal, and residual components, we
can better capture the underlying structure of the data, leading to more accurate predictions. This is especially
important when dealing with data that exhibits strong seasonal effects.
Detect Anomalies and Irregularities: Decomposition helps in identifying any unusual patterns or irregularities in
the residuals (the "noise" part of the data) that could indicate anomalies, outliers, or other issues that might require
further attention or corrective actions.
27
Data Analysis
Seasonal Decomposition
Figure. 10.
28
Data Analysis
Stationary Data
Why its important?
•
Ensures Reliable Modeling: Stationary data has constant statistical properties (mean, variance, and
autocorrelation) over time, which are critical for the validity of many time series models. Nonstationary data can lead to unreliable parameter estimates and poor forecasting accuracy.
•
Prevents Spurious Results: Stationarity eliminates the risk of spurious relationships in time series
analysis, ensuring that the results and insights are meaningful and not driven by trends or seasonality
inherent in non-stationary data.
Figure. 11.
Figure. 12.
29
Data Analysis
Stationarity Test
The Augmented Dickey-Fuller (ADF) test is a statistical test used to check the stationarity of a time series.
Specifically, it tests the null hypothesis (H0) that a unit root is present in the data, meaning the series is
non-stationary. The alternative hypothesis (Ha) is that the data does not have a unit root and is
therefore stationary.
Interpreting the ADF Test Results:
1.ADF Statistic:
• If the ADF test statistic is a large negative value (more negative than the critical value), you reject
the null hypothesis, indicating the series is stationary.
• If the ADF statistic is less negative or closer to zero, you fail to reject the null hypothesis,
meaning the series is non-stationary.
2.Critical Values:
• The test provides critical values at different significance levels (e.g., 1%, 5%, 10%).
• For example, at the 1% significance level, if the ADF statistic is below the critical value (e.g., -3.5),
the series is stationary at that confidence level.
30
Data Analysis
Stationarity Test
Heights
ADF statistics
80m
-18.69892043
100m
-18.75923105
120m
-23.41153468
140m
-22.42263589
160m
-23.74581069
180m
-23.88461172
Table. 1.
The large negative ADF values consistently
from 80m to 200m strongly indicate that the
200m observed for heights
-23.90481139
wind speed data at these heights is stationary, meaning its statistical properties (mean, variance, and
autocorrelation) remain constant over time, making it suitable for reliable modeling and analysis.
31
Deep Learning Models
A total of 7 deep learning models were applied.
Figure. 13.
32
Deep Learning Models
Models
Architecture
LSTM
LSTM layer: 50, Dense layer: 1, epochs = 5, loss = mse
BiLSTM
Bidirectional layers: 50, Dense layer: 1, epochs = 5, loss = mse
GRU
GRU Units: 50, Dense layer: 1, epochs = 5, loss = mse
Stacked LSTM
First layer: 50, Second layer: 50, Dense layer: 1, epochs = 5, loss = mse
CNN LSTM
Conv1D filters = 64,Max pooling = 2, LSTM = 50, Second Dense layer: 1, epochs = 5, loss = mse
ConvLSTM
filters = 64, Dense layer: 1, epochs = 5, loss = mse
CNN
Conv1D filters = 64,Max pooling = 2, First Dense layer = 50, Second Dense layer: 1, epochs = 5, loss = mse
Table. 2. Model Architecture
33
Performance Metrics
Five distinct performance indicators were employed to examine
the accuracy of the applied models. These consist of the R2 value,
root mean square error (RMSE), mean square error (MSE), mean
absolute percentage error(MAPE) and mean absolute error (MAE).
For a dataset with n data points, where yi represents the actual
value and yⅈ represents the model’s predicted value.
Mean Absolute
Error (MAE) =
1 n
Σ
y − yⅈ
n ⅈ=1 ⅈ
Mean Squared
Error (MSE) =
1
n
n
Σⅈ=1
yⅈ − yⅈ 2
Root Mean Squared
Error (RMSE) = MSE
=
1 n
Σ
y − yⅈ 2
n ⅈ=1 ⅈ
Mean Absolute
Percentage Error
(MAPE) =
1 𝑛
𝑦𝑖 −𝑦𝑖
𝛴
× 100
ⅈ=1
𝑛
𝑦
𝑖
R2 (Coefficient of
determination) =
2
Σn
ⅈ=1 yⅈ −yⅈ
1-Σn y −y 2
ⅈ
ⅈ=1 ⅈ
34
Results
For 80m hub height.
Height
80m
MAE
RMSE
MAPE
R2
LSTM
0.3827
0.5933
10.3933%
0.9479
BiLSTM
0.3144
0.5469
6.9528%
0.9557
GRU
0.3384
0.5598
7.8832%
0.9536
Stacked LSTM
0.2405
0.5176
5.8705%
0.9603
Conv LSTM
0.287
0.5379
6.1254%
0.9572
CNN LSTM
0.5085
0.6864
11.2262%
0.9302
CNN
0.2822
0.5506
6.5395%
0.9551
Models
For 80m hub height the Stacked LSTM model performed best amongst all as highlighted above.
Table. 3.
35
Results
For 80m hub height.
Stacked LSTM model
Figure. 14.
36
Results
For 100m hub height.
Height
Models
MAE
RMSE
MAPE
R2
100m
BiLSTM
0.2962
0.5343
6.1817%
0.9587
GRU
0.2657
0.5163
6.6416%
0.9615
Stacked LSTM
0.3267
0.5468
8.5328%
0.9568
Conv LSTM
0.2283
0.5049
5.5282%
0.9631
CNN LSTM
0.2721
0.5395
6.6313%
0.9579
CNN
0.3072
0.5517
6.6746%
0.9560
For 100m hub height the Conv LSTM model performed best amongst all as highlighted above.
Table. 4.
37
Results
For 100m hub height.
Conv LSTM model
Figure. 15.
38
Results
For 120m hub height.
Height
Models
MAE
RMSE
MAPE
R2
120m
LSTM
0.3275
0.5458
6.7361%
0.9581
BiLSTM
0.3359
0.5542
6.3079%
0.9568
GRU
0.2667
0.5183
5.7818%
0.9622
Stacked LSTM
0.2317
0.5150
5.0496%
0.9627
Conv LSTM
0.2345
0.5060
5.5518%
0.9640
CNN LSTM
0.278
0.5469
7.1564%
0.9579
CNN
0.2824
0.5467
6.4987%
0.9580
For 120m hub height the Conv LSTM model performed best amongst all as highlighted above.
Table. 5.
39
Results
For 120m hub height.
Conv LSTM model
Figure. 16.
40
Results
For 140m hub height.
Height
Models
MAE
RMSE
MAPE
R2
140m
LSTM
0.2857
0.5487
6.5994%
0.9584
BiLSTM
0.2444
0.5367
5.4492%
0.9602
GRU
0.2989
0.5513
6.2145%
0.9580
Stacked LSTM
0.2368
0.5372
5.5120%
0.9601
Conv LSTM
0.2925
0.5486
6.5110%
0.9584
CNN LSTM
0.3128
0.5852
8.8687%
0.9527
CNN
0.2744
0.5679
7.0406%
0.9555
For 140m hub height the BiLSTM model performed best amongst all as highlighted above.
Table. 6.
41
Results
For 140m hub height.
BiLSTM model
Figure. 17.
42
Results
For 160m hub height.
Height
Models
MAE
RMSE
MAPE
R2
160m
LSTM
0.2498
0.5358
5.6138%
0.9612
BiLSTM
0.2488
0.5332
5.6189%
0.9616
GRU
0.306
0.5558
5.9047%
0.9583
Stacked LSTM
0.3406
0.5707
7.7336%
0.9560
Conv LSTM
0.257
0.5382
6.1428%
0.9609
CNN LSTM
0.4006
0.6233
7.8882%
0.9475
CNN
0.2904
0.5822
7.9967%
0.9542
For 160m hub height the BiLSTM model performed best amongst all as highlighted above.
Table. 7.
43
Results
For 160m hub height.
BiLSTM model
Figure. 18.
44
Results
For 180m hub height.
Height
Models
MAE
RMSE
MAPE
R2
180m
LSTM
0.3132
0.6184
7.0050%
0.9491
BiLSTM
0.2611
0.6021
5.9011%
0.9517
GRU
0.3249
0.6253
7.0092%
0.9479
Stacked LSTM
0.3011
0.6206
8.1384%
0.9487
Conv LSTM
0.2803
0.6101
6.1625%
0.9504
CNN LSTM
0.4102
0.6728
8.9017%
0.9397
CNN
0.3407
0.6415
9.1412%
0.9452
For 180m hub height the BiLSTM model performed best amongst all as highlighted above.
45
Results
For 180m hub height.
BiLSTM model
Figure. 19.
46
Results
For 200m hub height.
Height
Models
MAE
RMSE
MAPE
R2
200m
LSTM
0.2807
0.6828
6.3882%
0.9373
BiLSTM
0.2793
0.6835
6.4155%
0.9372
GRU
0.3205
0.6851
7.2951%
0.9369
Stacked LSTM
0.3252
0.7100
7.0801%
0.9322
Conv LSTM
0.3654
0.7099
8.2730%
0.9322
CNN LSTM
0.3468
0.7080
8.3953%
0.9326
CNN
0.3678
0.7258
7.6748%
0.9292
For 200m hub height the LSTM model performed best amongst all as highlighted above.
Table. 8.
47
Results
For 200m hub height.
LSTM model
Figure. 20.
48
Conclusions
• Forecasting models in offshore wind speed prediction are critical for structural health monitoring of wind turbines, ensuring
their durability, optimizing performance, and maintaining the reliability of renewable energy contributions to the grid.
• Models such as Stacked LSTM and ConvLSTM demonstrated high accuracy at hub heights between 80m and 120m, with MAPEs
ranging from 5.52% to 5.87%, showcasing their ability to effectively capture spatial-temporal wind patterns.
• The Bidirectional LSTM model proved robust in handling long-term wind data dependencies, achieving MAPEs between 5.45%
and 5.9% at hub heights of 140m, 160m, and 180m.
• The LSTM model recorded a relatively higher MAPE of 6.39% at 200m, indicating opportunities for model refinement,
potentially through the inclusion of additional atmospheric variables like pressure and temperature.
49
Policy Implications

Accurate wind speed forecasting facilitates the identification of high-potential offshore wind sites, enabling
optimal resource allocation and enhancing the efficiency of offshore wind energy projects. This aligns with
India’s ambitious target of achieving 30 GW offshore wind capacity by 2030.

Reliable time series forecasting supports better grid integration, addressing the variability challenges
associated with renewable energy sources and ensuring grid stability.

The study's results hold the potential to guide investment decisions by reducing risks and uncertainties,
thereby encouraging private sector participation.

Policymakers can utilize these insights to refine energy planning strategies, reducing reliance on fossil fuels
and advancing the nation’s commitment to achieving net-zero emissions by 2070.
50
Policy Implications

Moreover, the implications extend to fostering local manufacturing hubs and job creation in coastal regions,
strengthening the economic fabric of the country.

This research contributes to designing resilient energy systems, capable of adapting to the impacts of climate change,
such as shifting wind patterns and rising sea levels.

Lastly, the study provides a robust data-driven foundation for refining and implementing policies like the National
Offshore Wind Energy Policy, thereby bolstering India’s renewable energy ecosystem. These findings underscore the role
of advanced forecasting techniques in shaping a sustainable and energy-secure future for the nation.
51
Limitations of the Study

Dependence on Data Quality and Availability: One primary challenge lies in the dependence of the predictive models on
the quality and availability of historical wind speed data. In regions where data is sparse, inconsistent, or collected at low
resolution, the accuracy and reliability of these models may be compromised. This limitation underscores the importance
of robust data collection frameworks and highlights the need for enhanced efforts to fill data gaps in underrepresented
regions.

Computational Complexity: Another limitation stems from the computational complexity of deep learning models like
BiLSTM, ConvLSTM, and CNN-LSTM. These models, while effective, require substantial computational resources, which
can pose challenges for real-time applications or deployment in resource-constrained environments. Future research
should optimize these models for efficiency without sacrificing accuracy, ensuring they remain scalable and accessible for
broader applications, including operational forecasting and policy planning.
52
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