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

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as indoor light energy. We focus on characterizing the energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for determining time fair energy allocation in systems with predictable energy inputs, as well as in systems where energy inputs are stochastic.

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Networking low-power energy harvesting devices: Measurements and algorithms

The contribution of power production from PV systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production, as a basis for management of the electricity grids and trading on the energy market. We present and evaluate the regional PV power prediction system of University of Oldenburg and Meteocontrol GmbH providing forecasts of up to 2 days ahead with hourly resolution. The proposed approach is based on forecasts of the global model of the European Centre for Medium-Range Forecasts (ECMWF). It includes a post-processing procedure to derive optimised, site-specific irradiance forecasts and explicit physical modelling steps to convert the predicted irradiances to PV power. Finally, regional power forecasts are derived by up-scaling from a representative set of PV systems. The investigation of proper up-scaling is a special focus of this paper. We introduce a modified up-scaling approach, modelling the spatial distribution of the nominal power with a resolution of 1° × 1°. The operational PV power prediction system is evaluated in comparison to the modified up-scaling approach for the control areas of the two German transmission system operators ‘transpower’ and ‘50 Hertz’ for the period 2.7.2009–30.4.2010. rmse values of the operational forecasts are in the range of 4–5% with respect to the nominal power for intra-day and day-ahead forecast horizons. Further improvement is achieved with the modified up-scaling approach. Copyright © 2010 John Wiley & Sons, Ltd.

Regional PV power prediction for improved grid integration

Recently, there has been an increase in concern about the global environment Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electrical power and heat are controlled with a communication network We developed the power generation forecast method for photovoltaic power systems in an energy network The method makes use of weather information and regression analysis We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan As a result of comparing measurements with prediction values, the average prediction error per day was about 26% of the measured power © 2009 Wiley Periodicals, Inc Electr Eng Jpn, 167(4): 16–23, 2009; Published online in Wiley InterScience (wwwintersciencewileycom) DOI 101002/eej20755

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/eej.20755

Forecasting electric power generation in a photovoltaic power system for an energy network

Mitsuru Kudo Member (NTT Energy and Environment System Laboratories, kudo.mitsuru@lab.ntt.co.jp) Akira Takeuchi Member (NTT Energy and Environment System Laboratories, takeuchi.akira@lab.ntt.co.jp) Yousuke Nozaki Member (NTT Energy and Environment System Laboratories, nozaki.yousuke@lab.ntt.co.jp) Hisahito Endo Member (NTT Energy and Environment System Laboratories, endo.hisahito@lab.ntt.co.jp) Jiro Sumita Member (NTT FACILITES,INC., sumita22@ntt-f.co.jp)

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Hisahito Endo

Papers

Forecasting electric power generation in a photovoltaic power system for an energy network

Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network