# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 1 Introduction  
Solar thermal or electrical systems require high quality solar radiation measurement instruments in order to accurately measure solar energy received on the plant. Poor quality data or too short data series can generate errors in plant design, performance, and production forecasting, negatively impacting return on investment. Unfortunately, measures of solar radiation are sparse and inaccurate over the world; there are still large areas without any solar radiation observations [1]. Investments and maintenance costs for each measurement site are not negligible and even in industrialized countries, the national network often consists in a relatively small number of solar radiation stations [2]; and the measurement quality varies from a network to another, often by lack of maintenance and calibration. The measuring devices' price is an important part of the process cost of collecting solar data, especially for non-profit institutions, such as schools or universities [2]. The amount of meteorological stations measuring solar irradiance through the world is difficult to count because various sources give different information [3]. Even so, only 1000 continental stations around the world are measuring solar radiation [4]. In these conditions, it is interesting to look for some relations between sparse solar radiation data and more measured meteorological data as temperature, humidity, and wind speed. Satellite observations are used for determining the solar irradiation on the ground, with a good accuracy, but the time step of the estimated data is relatively large (minimum hourly). A bibliographical study showed [5] that artificial neural networks (ANN) were developed between meteorological parameters and solar irradiations, but generally only for averaged values of solar irradiations (on a monthly or annual basis); today, data with a short time granularity (minute, 5-min) must be known for interesting applications in solar energy. In the first part of this paper, 10 meteorological inputs (measured or calculated) are available and then \(2^{10}-1=1023\) combinations of input data are possible, from 10 ANN with only one input to one ANN with 10 inputs. All these combinations are tested and the best configurations are discussed. The solar panels are rarely fixed horizontally and the global tilted irradiation (GTI) is rarely measured and must be estimated from global horizontal irradiation (GHI), more often available; using pyranometers with various inclinations is costly and their maintenance is constraining. Thus, it is useful to develop accurate methods for determining GTI from only GHI. This objective is difficult to reach with conventional physical relations because the sky anisotropy makes the modeling of the sky diffuse radiation difficult [6; 7; 8]. ANN methods generally realize the same conversion with an improved adequacy (generally, at an hourly scale), and they outperform [9; 10; 11; 12; 13] the traditional methods due to the inherent non-linearity in solar radiation data. In this work, an ANN model is developed and optimized for \"tilting\" 5-min time step solar data. The stochastic and intermittent behavior of the solar resources poses numerous problems for the electricity grid operator [5] and limits the future development of the photovoltaic (PV) and concentrated solar power (CSP) plants. To improve the integration of such systems, the solution consists in introducing energy storages and developing smart grids as well as implementing production and consumption forecasting. The forecasting of the output power of solar energy systems is required for good operation of the power grid and for optimal management of electrical flows [3]. It is essential to estimate the energy reserves, to schedule the power systems, to optimally manage the storage, and to trade in the electricity market [14; 15; 16]. Thus, predicted and anticipated events are easier to manage.