A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting
نویسندگان
چکیده
We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.
منابع مشابه
Machine learning for solar irradiance forecasting of photovoltaic system
Photovoltaic generation of electricity is an important renewable energy source, and large numbers of relatively small photovoltaic systems are proliferating around the world. Today it is widely acknowledged by power producers, utility companies and independent system operators that it is only through advanced forecasting, communications and control that these distributed resources can collectiv...
متن کاملEconomic Appraisal of Investment Projects in Solar Energy under Uncertainty via Fuzzy Real Option Approach (Case Study: a 2-MW Photovoltaic Plant in South of Isfahan, Iran)
Investment in renewable energies especially solar energies is encountered with numerous uncertainties considering the increased dynamism in economic and financial conditions and makes investment in this field irreversible to a large extent, paying attention to modern methods of economic appraisal of such investments is highly important. A framework is provided in the current study in order to e...
متن کاملImproved Prediction Approach on Solar Irradiance of Photovoltaic Power Station
Prediction of solar irradiance has great significance to photovoltaic power forecasting and the scheduling plan of power generation. Aim at unsatisfactory prediction accuracy of traditional forecasting methods, this paper presents an approach to predict solar irradiance of photovoltaic power station based on wavelet decomposition and extreme learning machine. With historical irradiance sequence...
متن کاملA Machine Learning Approach to Predict Solar Radiation for Solar Energy Based
Solar energy is used in many applications, such as increasing water’s temperature or moving electrons in a photovoltaic cell, agriculture planning, fuel production, electricity production, transport, architecture and urban planning, etc. Solar energy is secure, clean, and available on the Earth throughout the year. Its secure and clean applications are very important to the world, especially at...
متن کاملInterval-based Solar PV Power Forecasting Using MLP-NSGAII in Niroo Research Institute of Iran
This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...
متن کامل