Review of photovoltaic power forecasting

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

Solar photovoltaic generation forecasting methods: A review

With the continuous increase of wind power penetration in power systems, the problems caused by the volatile nature of wind speed and its occurrence in the system operations such as scheduling and dispatching have drawn attention of system operators, utilities and researchers towards the state-of-the-art wind speed and power forecasting methods These methods have the required capability of reducing the influence of the intermittent wind power on system operations as well as of harvesting the wind energy effectively In this context, combining different methodologies in order to circumvent the challenging model selection and take advantage of the unique strength of plausible models have recently emerged as a promising research area Therefore, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance Aiming to fill the mentioned research gap, this paper outlines the combined forecasting approaches and presents an up-to date annotated bibliography of the wind forecasting literature Furthermore, the paper also points out the possible further research directions of combined techniques so as to help the researchers in the field develop more effective wind speed and power forecasting methods

A review of combined approaches for prediction of short-term wind speed and power

Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for the system reliability and promoting large scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machine. In the process, the weather conditions are firstly divided into four types which are clear sky, cloudy day, foggy and rainy day. One-day-ahead PV power output forecasting model for single station is derived based on the weather forecasting data and historically actual power output data as well as the principle of Support Vector Machine (SVM). After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected photovoltaic systems is effective and promising.

Forecasting power output of photovoltaic system based on weather classification and support vector machine

The prediction algorithm is an important key factor in wind power prediction. However, there are pros and cons on different forecasting algorithms. Based on the principles of wavelet transform (WT), support vector machine (SVM) as well as characteristics of wind turbine generation systems, two prediction methods are presented and compared in this paper. In method 1, the time series of model input are decomposed into different frequency composes and models are set up separately based on SVM. The results are combined together to obtain the final wind power output. In method 2, the wavelet kernel function is applied in place of RBF kernel function in SVM training. To supply more valuable suggestions, the means of evaluating prediction algorithm precision is proposed. The operation data from two wind farms both in North China and U.S.A are used to test the usability of the method. The mean relative error of WT-SVM model (method 1) is less than that of traditional SVM model.

Short-term wind power prediction based on wavelet transform-support vector machine and statistic characteristics analysis

Very-short term wind power forecasting is one of the most effective ways to deal with the challenges of increased penetration of wind power into the electric grid due to its fluctuation and volatility. To improve wind power forecasting by taking advantage of each independent forecasting model, a hybrid model is proposed by means of grey relational analysis and wind speed distribution features. The weight of each independent model is estimated according to different wind speed subsection and similar wind speed frequency. The case study shows that the hybrid forecasting model has broader applications in very-short term (15-minute-ahead) wind power output forecasting.

Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features

Capacity and output power forecasting have great significance in seamless integration of renewable energy to the grid. However, the uncertainty of wind power and intermittence of wind energy are the main factors which affect forecasting precision. The wind power output data can be treated as a signal stream which has characteristics for possible wind capacity forecasting. Hilbert-Huang Transforms (HHT) and Hilbert spectral analysis have been applied extensively to analysis nonlinear and non-stationary stochastic signal. The time series of wind power output has been transformed into certain signals with different frequencies. Each signal is taken as input data joining with wind speed data to establish Artificial Neural Network (ANN) forecasting model. The models are combined together to obtain the final results on potential wind power output. This paper proposes HHT-ANN model for wind power forecasting. A case study of a wind farm in Texas, U.S shows that the MRE of proposed method is lower than the traditional ANN approach.

Yongping Yang

Papers

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Forecasting power output of photovoltaic system based on weather classification and support vector machine

Short-term wind power prediction based on wavelet transform-support vector machine and statistic characteristics analysis

Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features

Short term wind power forecasting using Hilbert-Huang Transform and artificial neural network