مقاله انگلیسی رایگان در مورد یک سیستم هوشمند ترکیبی برای پیش بینی تولید انرژی خورشیدی – الزویر 2019

1- Introduction and previous work

2- Hybrid Intelligent System

3- Case Study: solar energy prediction

4- Results & discussion

5- Conclusions and future work

References

Abstract

There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a real-world installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energy produced by the panel with an error that was lower than 10−4 in 86% of cases.

Introduction and previous work

Renewable Energy (RE) has a key role to play in the field of increased sustainability and is one of the most relevant technologies in that respect [1]. As a consequence, many buildings and especially new ones now incorporate RE facilities. This general European trend has also been applicable to Spain over recent years, where the rate of new RE installations, in general, and solar thermal energy, in particular, is increasing. One reason is found in the Spanish legal regulation on this matter [2], which states that solar system installations are mandatory in new buildings. Obviously, the energy generated by these installations implies a saving on other sources of energy that would otherwise have been used for that purpose. If the energy needs are known in advance, such information may be used to predict the energy thresholds and when the available energy will no longer be demanded. [3]. As a result, it will be possible to take corrective actions with the purpose of reducing the energy from non-renewable energy sources. Various works have addressed that challenge in different ways. In [4], a new strategy was proposed for the optimal scheduling problem, taking into account the impact of uncertainties in wind, solar PV, and load demand forecasts.