Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models

Abstract

Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.

Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions

“…A neural network is analogous to the human brain in two aspects. First, the network learns from inputs during the process [54], [55]. Second, the information is recorded via synaptic weights, indicating the connection strength between the neurons.…”

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

“…In [14], Fentis et al used Feed Forward Neural Network and Least Square Support Vector Regression with exogenous inputs to perform short-term forecasting of PV generation. In [15] analyzed the performances of (Autoregressive Integrated Moving Average) ARIMA and Artificial Neural Network (ANN) for forecasting the PV energy generation. In [16], Atique et al used ARIMA with parameter selection based on Akaike information criterion and the sum of the squared estimate to forecast PV generation.…”

“…We now compare the forecasting performance of rTPNN with the performances of LSTM, MLP, Linear Regression, Lasso, Ridge, ElasticNet, Random Forest as well as 1-Day Naive Forecast. 1 Recall that in recent literature, References [31,32,33] used LSTM, and Reference [14,15,19,38]…”

An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids

“…Focusing on PV production forecasting, many popular regression models have been proposed, including traditional time-series ARIMA models [18], decision-tree-based models [19], support vector machines (SVM) [20], and artificial neural networks (ANNs) [21], among others. Recent studies indicate that DL models result in better forecasting accuracy compared to purely statistical models and simple ML models, but this cannot be generalized for all cases [22].…”