A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
نویسندگان
چکیده
The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.
منابع مشابه
Short and Mid-Term Wind Power Plants Forecasting With ANN
In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...
متن کاملShort and Mid-Term Wind Power Plants Forecasting With ANN
In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...
متن کاملApplication of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets
Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity...
متن کاملA Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks
The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (A...
متن کاملShort-term and Medium-term Gas Demand Load Forecasting by Neural Networks
The ability of Artificial Neural Network (ANN) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real concern. As the most applicable network, the ANN with multi-layer back propagation perceptrons is used to approximate functions. Throughout the current work, the daily effective temperature is determined, and then the weather data w...
متن کامل