Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data

The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a numerical weather prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An artificial neural network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available.

[1]  Masayu Norman,et al.  A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX) , 2018, Journal of Atmospheric and Solar-Terrestrial Physics.

[2]  Kok Soon Tey,et al.  Forecasting of photovoltaic power generation and model optimization: A review , 2018 .

[3]  Francisco Troncoso-Pastoriza,et al.  Prediction of Building’s Thermal Performance Using LSTM and MLP Neural Networks , 2020, Applied Sciences.

[4]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[5]  Yahya Z. Alharthi,et al.  Resource Assessment and Techno-Economic Analysis of a Grid-Connected Solar PV-Wind Hybrid System for Different Locations in Saudi Arabia , 2018, Sustainability.

[6]  Konstantin Eckle,et al.  A comparison of deep networks with ReLU activation function and linear spline-type methods , 2018, Neural Networks.

[7]  Pengxiang Qiu,et al.  Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater , 2020, Water.

[8]  A. Holtslag,et al.  Downscaling daily air-temperature measurements in the Netherlands , 2020, Theoretical and Applied Climatology.

[9]  M. G. De Giorgi,et al.  Performance measurements of monocrystalline silicon PV modules in South-eastern Italy , 2013 .

[10]  Luca Massidda,et al.  Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany , 2017 .

[11]  WoonSeong Jeong,et al.  Study on Solar Radiation Models in South Korea for Improving Office Building Energy Performance Analysis , 2016 .

[12]  Masahiro Kazumori,et al.  All‐sky satellite data assimilation at operational weather forecasting centres , 2018 .

[13]  Pablo Eguía Oller,et al.  Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping , 2020, Infrastructures.

[14]  Adel Mellit,et al.  Short-term forecasting of power production in a large-scale photovoltaic plant , 2014 .

[15]  Francis X. Giraldo,et al.  Current and Emerging Time-Integration Strategies in Global Numerical Weather and Climate Prediction , 2019 .

[16]  Marco Mussetta,et al.  Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks , 2020, Energies.

[17]  Maria Grazia De Giorgi,et al.  Photovoltaic power forecasting using statistical methods: impact of weather data , 2014 .

[18]  M Malvoni,et al.  Data on photovoltaic power forecasting models for Mediterranean climate , 2016, Data in brief.

[19]  Tugrul U. Daim,et al.  Using artificial neural network models in stock market index prediction , 2011, Expert Syst. Appl..

[20]  Walter Richardson,et al.  A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting , 2017 .

[21]  Siamak Mehrkanoon,et al.  Deep Shared Representation Learning for Weather Elements Forecasting , 2019, BNAIC/BENELEARN.

[22]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[23]  Francesco Grimaccia,et al.  Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power , 2017, Math. Comput. Simul..

[24]  Elena Arce Fariña,et al.  Use of a numerical weather prediction model as a meteorological source for the estimation of heating demand in building thermal simulations , 2020 .

[25]  Fei Wang,et al.  Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters , 2012 .

[26]  Joao Gari da Silva Fonseca Junior,et al.  Forecasting Regional Photovoltaic Power Generation - A Comparison of Strategies to Obtain One-Day-Ahead Data , 2014 .