99 research outputs found
Wind power forecasting with machine learning: single and combined methods
In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.info:eu-repo/semantics/publishedVersio
Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM
Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolution Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results
Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM
Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results
Assessment of Renewable Energy Resources with Remote Sensing
The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii
Fernando Ramos Martins
Editorial for the Special Issue: Assessment of Renewable Energy Resources with
Remote Sensing
Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1
André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira
Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil
Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7
Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller
On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region
Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33
JoaquĂn Alonso-Montesinos
Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera
Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43
Román MondragĂłn, JoaquĂn Alonso-Montesinos, David Riveros-Rosas, Mauro ValdĂ©s, HĂ©ctor EstĂ©vez, Adriana E. González-Cabrera and Wolfgang Stremme
Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area
Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61
Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island
Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79
Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao
Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101
Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov
Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125
Ian R. Young, Ebru Kirezci and Agustinus Ribal
The Global Wind Resource Observed by Scatterometer
Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147
Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura
Coastal Wind Measurements Using a Single Scanning LiDAR
Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165
Cristina Sáez Blázquez, Pedro Carrasco GarcĂa, Ignacio MartĂn Nieto, MiguelAngel ´ MatĂ©-González, Arturo Farfán MartĂn and Diego González-Aguilera
Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods
Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189
Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz
A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data
Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma
An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data
Drought is a serious natural disaster that has a long duration and a wide
range of influence. To decrease the drought-caused losses, drought prediction
is the basis of making the corresponding drought prevention and disaster
reduction measures. While this problem has been studied in the literature, it
remains unknown whether drought can be precisely predicted or not with machine
learning models using weather data. To answer this question, a real-world
public dataset is leveraged in this study and different drought levels are
predicted using the last 90 days of 18 meteorological indicators as the
predictors. In a comprehensive approach, 16 machine learning models and 16 deep
learning models are evaluated and compared. The results show no single model
can achieve the best performance for all evaluation metrics simultaneously,
which indicates the drought prediction problem is still challenging. As
benchmarks for further studies, the code and results are publicly available in
a Github repository.Comment: Github link:
https://github.com/jwwthu/DL4Climate/tree/main/DroughtPredictio
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022
Nowadays, wind energy has drawn increasing attention as its important role in
carbon neutrality and sustainable development. When wind power is integrated
into the power grid, precise forecasting is necessary for the sustainability
and security of the system. However, the unpredictable nature and long sequence
prediction make it especially challenging. In this technical report, we
introduce the BERT model applied for Baidu KDD Cup 2022, and the daily
fluctuation is added by post-processing to make the predicted results in line
with daily periodicity. Our solution achieves 3rd place of 2490 teams. The code
is released athttps://github.com/LongxingTan/KDD2022-Baid
Solar Flare Prediction and Feature Selection using Light Gradient Boosting Machine Algorithm
Solar flares are among the most severe space weather phenomena, and they have
the capacity to generate radiation storms and radio disruptions on Earth. The
accurate prediction of solar flare events remains a significant challenge,
requiring continuous monitoring and identification of specific features that
can aid in forecasting this phenomenon, particularly for different classes of
solar flares. In this study, we aim to forecast C and M class solar flares
utilising a machine-learning algorithm, namely the Light Gradient Boosting
Machine. We have utilised a dataset spanning 9 years, obtained from the
Space-weather Helioseismic and Magnetic Imager Active Region Patches (SHARP),
with a temporal resolution of 1 hour. A total of 37 flare features were
considered in our analysis, comprising of 25 active region parameters and 12
flare history features. To address the issue of class imbalance in solar flare
data, we employed the Synthetic Minority Oversampling Technique (SMOTE). We
used two labeling approaches in our study: a fixed 24-hour window label and a
varying window that considers the changing nature of solar activity. Then, the
developed machine learning algorithm was trained and tested using forecast
verification metrics, with an emphasis on evaluating the true skill statistic
(TSS). Furthermore, we implemented a feature selection algorithm to determine
the most significant features from the pool of 37 features that could
distinguish between flaring and non-flaring active regions. We found that
utilising a limited set of useful features resulted in improved prediction
performance. For the 24-hour prediction window, we achieved a TSS of 0.63
(0.69) and accuracy of 0.90 (0.97) for C (M) class solar flares.Comment: Accepted for publication in Solar Physics journa
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization
We investigate nonlinear prediction in an online setting and introduce a
hybrid model that effectively mitigates, via an end-to-end architecture, the
need for hand-designed features and manual model selection issues of
conventional nonlinear prediction/regression methods. In particular, we use
recursive structures to extract features from sequential signals, while
preserving the state information, i.e., the history, and boosted decision trees
to produce the final output. The connection is in an end-to-end fashion and we
jointly optimize the whole architecture using stochastic gradient descent, for
which we also provide the backward pass update equations. In particular, we
employ a recurrent neural network (LSTM) for adaptive feature extraction from
sequential data and a gradient boosting machinery (soft GBDT) for effective
supervised regression. Our framework is generic so that one can use other deep
learning architectures for feature extraction (such as RNNs and GRUs) and
machine learning algorithms for decision making as long as they are
differentiable. We demonstrate the learning behavior of our algorithm on
synthetic data and the significant performance improvements over the
conventional methods over various real life datasets. Furthermore, we openly
share the source code of the proposed method to facilitate further research
Application of Machine Learning to Predict Electricity Demand from Electric Vehicles in Workplace Settings
As sustainability-oriented policies begin to be implemented across the world, adapting the current electric power system (EPS) to meet the demands required by those poli- cies is key to meeting emissions targets. Part of those policies includes the continued expansion of the electrification of national transportation systems. This electrifica- tion of transportation will require the vast expansion of electric vehicle (EV) usage as well as the charging networks that will give them power. The consequent growth in anticipated energy demand must be included in infrastructure planning. As a result, forecasting the charging demand of EVs will be a vital tool to plan for the develop- ment of EPS infrastructure. The identification of the best forecasting methods is a key field of research supporting this effort. This thesis analyzed several statistical models and state-of-the-art (SoA) deep learning (DL) machine learning models to determine relevant forecasting tools for predicting EV charging loads in the context of workplace charging. Workplace charging was identified as a gap in research, where fewer attempts to model EV demand at office buildings and places of work had been recorded. The data set chosen was the NREL workplace charging data set, which included daily charging load from 2017-2020. The time series forecasting models tested include ARIMA, SARIMA, XGBoost, LightGBM, RNN, LSTM, GRU, TFT, and N-BEATS. A machine learning modelling pipeline was developed for each model. Results of modelling determined that the SoA DL models TFT and N-BEATS were the top performing models with a mean average percentage error (MAPE) score of 18.9% and 19.5%, respectively, followed by XGBoost with an MAPE of 21.1%. From a residual error analysis, it was found that TFT poorly estimated peak consumption, but was able to more consistently predict the general trends, as compared to XGBoost and N-BEATS, which performed better with extreme fluctuations, but struggled with non-extreme value
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