Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

Abstract

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.

Recent Improvements of the PV Solar Energy Generation Performance

“…Moreover, the tracking of MPP in this method without a well-skilled operator is inaccurate because the FLC function depends on the operator's ability [21]. The Artificial Neural Network (ANN) [84] is also an AI-based method with having multilayer perceptrons (MLP) such as an input layer that collects inputs, a hidden layer, and an output layer that issues outputs. It also has positive effects on handling the nonlinearity behavior of PV solar modules and on the onward movement of the techniques used to improve the PV solar energy generation.…”

“…c. Types of materials PV panels made by Different researches and developments are on-going on the manufacturing of solar cells to achieve better power output and efficiency of solar energy harnessing systems [83][84][85][86][87]. Mainly, the PV solar modules efficiency is decided by two factors: PV solar cell efficiency and total PV solar module efficiency.…”

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

“…The results showed that the RBNN generates low errors compared with the other techniques. The Internet of Things (IoT) was implemented to collect different natural factors in the environment to forecast the generated energy from a photovoltaic module using ANN [12]. The technique reduced the mean square error (MSE) while improving the forecasting effectiveness.…”

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

“…Among the systems developed, the prediction model using the Neural Network is the most widely applied. There are many new discoveries successfully obtained by this method [3][4]. Therefore, this paper will review some of the results of the past studies that have been conducted on the development of solar radiation prediction systems using Neural Network techniques.…”