PERAMALAN KECEPATAN ANGIN MENGGUNAKAN METODE EXPONENTIAL SMOOTHING DAN ARTIFICIAL NEURAL NETWORK (ANN) UNTUK RENCANA PENGAPLIKASIAN PADA PLTB (PEMBANGKIT LISTRIK TENAGA BAYU) DI KOTA BANDUNG

Energi angin merupakan sumber energi yang ramah lingkungan dan efisien yang populer saat ini. Energi angin dapat dikonversikan menjadi energi listrik untuk memenuhi kebutuhan energi listrik di masyarakat. Penelitian ini memaparkan hasil peramalan kecepatan angin kota Bandung selama 5 tahun kedepan yang bertujuan untuk mengetahui potensi kecepatan angin di kota Bandung dan rencana pengaplikasian PLTB (Pembangkit Listrik Tenaga Bayu) demi memenuhi kebutuhan listrik masyarakat, menggunakan metode perhitungan Exponential Smoothing dan Artificial Neural Network (ANN). Proses simulasi perhitungannya menggunakan software Zaitun time-series. Berdasarkan hasil simulasi, nilai peramalan yang paling sedikit nilai errornya didapatkan dengan menggunakan metode Artificial Neural Network (ANN). Hasil penelitian menentukan bahwa Kota Bandung tidak cocok didirikan PLTB karena tidak memenuhi batas minimum kecepatan angin yang cocok untuk sebuah PLTB. Kata kunci : Energi Angin, peramalan, Artificial Neural Network, Exponential Smoothing. ABSTRACT Wind Energy is an environmentally friendly and efficient energy source that is popular today. Wind energy can be converted into electrical energy to meet the electricity needs of the community. This study describes the results of Bandung wind speed forecasting for the next 5 years which are intended to determine the wind speed potential in Bandung and the plan for the application of PLTB (Bayu Power Plant) according to the electricity needs of the community, using the Exponential Smoothing and Artificial Neural Network reporting method (ANN ). The calculation simulation process uses the Zaitun time-series software. Based on the simulation results, the forecasting value with the least error value is obtained using the Artificial Neural Network (ANN) method. The results of the study determined that Bandung City was not suitable for PLTB because it did not meet the minimum limit for speed suitable for PLTB. Keyword : Wind Energy, Forecasting, Artificial Neural Network, Eksponential Smoothin

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

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Predictive control of wind turbines by considering wind speed forecasting techniques

A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. In this paper, wind speed forecasting techniques will be considered for predictive optimum control system of wind turbines

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version