Prediksi Kecepatan Angin 12 Jam Kedepan Menggunakan Automatic Weather Observing System (AWOS) Berbasis Regresi Linear

Automatic equipment for monitoring weather conditions such as the Automatic Weather Observing System (AWOS) is urgently needed by a meteorologist for the purposes of serving aviation weather services at airports. One of the most important information besides the weather for flight services is wind speed. This study integrates AWOS and linear regression models to predict wind speed parameters for the next 12 hours. These parameters are the lowest, average, and highest wind speed. The computational load required for building and training the proposed model system is determined by the duration the computer executes the model training commands and generates predictions. The wind speed hours ahead is assumed to be influenced by the condition of the previous weather parameters. Therefore, in this study, a scheme was tested using the length of historical data of different weather parameters to predict the wind speed parameters for the next 12 hours. The predictions generated are in summary form, i.e., the lowest speed, average speed and highest speed in that period. After testing it was found that the duration of the computer to train the model is 1.2 seconds and to generate predictions is 1.1 seconds. Meanwhile, the best scheme for generating predictions is linear regression with a predictor of 12 hours which produces an RMSE error of 0.63, 1.14, and 3.07 for the lowest wind speed, average wind, and highest wind respectively. These results indicate that the proposed model only requires a light computational load and can produce accurate predictions of wind speed parameters for the next 12 hours.Peralatan otomatis untuk pemantauan keadaan cuaca seperti Automatic Weather Observing System (AWOS) sangat dibutuhkan oleh seorang ahli meteorologi untuk keperluan melayani operasi penerbangan di bandara. Salah satu informasi yang penting selain cuaca untuk layanan penerbangan adalah kecepatan angin. Penelitian ini mengintegrasikan AWOS dan model regresi linear untuk memprediksi parameter kecepatan angin 12 jam kedepan. Parameter tersebut yaitu kecepatan angin terendah, rata – rata, dan tertinggi. Beban komputasi yang diperlukan untuk pembangunan dan pelatihan sistem model yang diajukan ditentukan oleh durasi komputer mengeksekusi perintah pelatihan model dan menghasilkan prediksi. Kecepatan angin kedepan diasumsikan dipengaruhi oleh keadaan parameter cuaca sebelumnya. Oleh karena itu pada penelitian ini, diuji skema penggunaan panjang data historis parameter cuaca yang berbeda untuk memprediksi parameter kecepatan angin 12 jam kedepan. Prediksi yang dihasilkan adalah dalam bentuk ringkasannya, yaitu kecepatan terendah, kecepatan rata – rata dan kecepatan tertinggi pada periode tersebut. Setelah uji coba didapatkan bahwa durasi komputer melatih model adalah 1.2 detik dan untuk menghasilkan prediksi adalah 1.1 detik. Sementara itu, skema terbaik untuk menghasilkan prediksi adalah regresi linear dengan prediktor 12 jam yang menghasilkan galat RMSE 0.63, 1.14, dan 3.07 untuk kecepatan angin terendah dan angin rata – rata, serta angin tertinggi secara berurutan. Hasil ini menunjukkan bahwa model yang diajukan hanya memerlukan beban komputasi yang ringan dan dapat menghasilkan prediksi parameter kecepatan angin 12 jam kedepan yang akurat

A two-stage framework for short-term wind power forecasting using different feature-learning models

With the growing dependence on wind power generation, improving the accuracy of short-term forecasting has become increasingly important for ensuring continued economical and reliable system operations. In the wind power forecasting field, ensemble-based forecasting models have been studied extensively; however, few of them considered learning the features from both historical wind data and NWP data. In addition, the exploration of the multiple-input and multiple-output learning structures is lacking in the wind power forecasting literature. Therefore, this study exploits the NWP and historical wind data as input and proposes a two-stage forecasting framework on the shelf of moving window algorithm. Specifically, at the first stage, four forecasting models are constructed with deep neural networks considering the multiple-input and multiple-output structures; at the second stage, an ensemble model is developed using ridge regression method for reducing the extrapolation error. The experiments are conducted on three existing wind farms for examining the 2-h ahead forecasting point. The results demonstrate that 1) the single-input-multiple-output (SIMO) structure leads to a better forecasting accuracy than the other threes; 2) ridge regression method results in a better ensemble model that is able to further improve the forecasting accuracy, than the other machine learning methods; 3) the proposed two-stage forecasting framework is likely to generate more accurate and stable results than the other existing algorithms

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

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

Multi-resolution spatio-temporal prediction with application to wind power generation

Wind energy is becoming an increasingly crucial component of a sustainable grid, but its inherent variability and limited predictability present challenges for grid operators. The energy sector needs novel forecasting techniques that can precisely predict the generation of renewable power and offer precise quantification of prediction uncertainty. This will facilitate well-informed decision-making by operators who wish to integrate renewable energy into the power grid. This paper presents a novel approach to wind speed prediction with uncertainty quantification using a multi-resolution spatio-temporal Gaussian process. By leveraging information from multiple sources of predictions with varying accuracies and uncertainties, the joint framework provides a more accurate and robust prediction of wind speed while measuring the uncertainty in these predictions. We assess the effectiveness of our proposed framework using real-world wind data obtained from the Midwest region of the United States. Our results demonstrate that the framework enables predictors with varying data resolutions to learn from each other, leading to an enhancement in overall predictive performance. The proposed framework shows a superior performance compared to other state-of-the-art methods. The goal of this research is to improve grid operation and management by aiding system operators and policymakers in making better-informed decisions related to energy demand management, energy storage system deployment, and energy supply scheduling. This results in potentially further integration of renewable energy sources into the existing power systems

DEWP: Deep Expansion Learning for Wind Power Forecasting

Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, etc. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP, a novel Deep Expansion learning for Wind Power forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Finally, we present extensive experiments in the real-world wind power forecasting application on two datasets from two different turbines to demonstrate the effectiveness of our approach.Comment: Accepted by TKD

Renewable energy sources integration via machine learning modelling: A systematic literature review

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms of costs and technology, expecting a massive diffusion in the near future and placing several challenges to the power grid. Since RESs depend on stochastic energy sources —solar radiation, temperature and wind speed, among others— they introduce a high level of uncertainty to the grid, leading to power imbalance and deteriorating the network stability. In this scenario, managing and forecasting RES uncertainty is vital to successfully integrate them into the power grids. Traditionally, physical- and statistical-based models have been used to predict RES power outputs. Nevertheless, the former are computationally expensive since they rely on solving complex mathematical models of the atmospheric dynamics, whereas the latter usually consider linear models, preventing them from addressing challenging forecasting scenarios. In recent years, the advances in machine learning techniques, which can learn from historical data, allowing the analysis of large-scale datasets either under non-uniform characteristics or noisy data, have provided researchers with powerful data-driven tools that can outperform traditional methods. In this paper, a systematic literature review is conducted to identify the most widely used machine learning-based approaches to forecast RES power outputs. The results show that deep artificial neural networks, especially long-short term memory networks, which can accurately model the autoregressive nature of RES power output, and ensemble strategies, which allow successfully handling large amounts of highly fluctuating data, are the best suited ones. In addition, the most promising results of integrating the forecasted output into decision-making problems, such as unit commitment, to address economic, operational and managerial grid challenges are discussed, and solid directions for future research are provided

Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy)

Advanced forecasting algorithms for renewable power systems

1 online resource (x, 112 pages) : illustrations (some colour), charts (some colour), graphs (some colour)Includes abstract.Includes bibliographical references (pages 100-112).Wind and solar power prediction is a challenging but important area of research. The thesis you described explores various statistical models and deep learning methods to improve the accuracy of wind speed and solar radiation predictions. The use of autoregressive integrated moving average (ARIMA) models, long short-term memory (LSTM) based recurrent neural network (RNN) models, and multilayer perceptron (MLP) neural networks were studied to predict future wind speed values and the performance of a photovoltaic (PV) system. The results showed that the proposed models can effectively improve the accuracy of wind speed and solar radiation prediction and that the LSTM network outperformed the MLP network in predicting solar radiation and energy for different time periods. It is important to note that the performance of the models may vary depending on the specific dataset used, the hyperparameters, and the model architecture. Therefore, it is essential to carefully tune these parameters to achieve the best possible performance. Accurately predicting the performance of a PV system at short time intervals is particularly important in the context of renewable energy sources, as it can help optimize the usage of these resources and improve overall efficiency. This research can contribute to the development of more accurate and reliable prediction models, which can lead to more efficient use of wind and solar power, reduce costs, and promote the adoption of renewable energy sources