Transfer learning for short-term wind speed prediction with deep neural networks

Abstract

a b s t r a c tAs a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique.

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

“…The principle of this signal processing method is to decompose the original time series with various fluctuations into a stationary one with different characteristics. Each series that is obtained after decomposition is treated as an intrinsic mode function (IMF), which satisfies the following two conditions: (1) in the whole time range, the number of local extremal points and over zero must be equal, or the maximum difference is one; and (2) the mean value of the two envelopes formed by the local maxima and local minima, respectively, is zero at any point.…”

“…Wind power, as a type of sustainable and clean energy, is one of the most widely used, technologically mature, and commercially produced renewable sources [1,2]. According to the Global Wind Energy Council (GWEC), the cumulative wind generating installed capacity has reached 486,790 MW at the end of 2016 with the share of 34.7% donated by China [3].…”

Intelligent Spectrum Allocating Scheme in Multi-RATs Networks

“…Back propagation (BP) neural network (NN) is a kind of multilayer feed forward neural network [16][17][18][19][20], usually has one or more hidden layers and one output layer, its transfer function is a S function that can be carried everywhere, as well as it has a strong mapping ability and can be used to approximate any nonlinear function. In the BP neural network, the signal propagation is moving forward, and error propagates backward from the last output layer in the opposite direction of the input conveyor after contrasting with the expected outputs.…”

Short-term wind power prediction based on extreme learning machine with error correction

“…Neural network (NN) models have been widely applied in a variety of business fields including accounting, management information systems, marketing, and production management. Many researchers focus on the improvement of NN, including recurrent NN, deep NN and so on [9][10][11][12][13][14]. Extreme Learning Machine(ELM) is based on a single-hidden layer feed-forward neutral network and only needs to calculate random weight between inputting layer and hidden layer.…”