Ultra-Short-term Photovoltaic Output Power Forecasting using Deep Learning Algorithms

B. Dimd, S. Völler, O. Midtgård, Tarikua Mekashaw Zenebe
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Abstract

Photovoltaic (PV) is becoming an attractive alternative in Norway in new zero-emission housing projects and in connection with hydropower reservoirs. However, fast-moving clouds result in abrupt changes in PV output power, which makes grid integration in such areas more challenging. One solution is to forecast the amount and variation of PV output power in advance. This paper, therefore, evaluates the performance of various DL (Deep Learning)-based forecasting models for a 20.15 kWp PV plant in Trondheim, Norway. The results show that a forecast model based on LSTM (Long Short-term Memory) network gives better performance in terms of RMSE (Root Mean Squared Error) for 15 minutes ahead forecast. This study can serve as the groundwork for future research into techniques and approaches that can result in a high-performing forecast model both in terms of accuracy and stability for the Norwegian climate.
基于深度学习算法的超短期光伏输出功率预测
光伏(PV)正在成为挪威新的零排放住房项目和与水电站水库相关的有吸引力的替代方案。然而,快速移动的云层导致光伏输出功率突变,这使得这些地区的电网整合更具挑战性。一种解决方案是提前预测光伏输出功率的大小和变化。因此,本文对挪威特隆赫姆20.15 kWp光伏电站的各种基于深度学习的预测模型的性能进行了评估。结果表明,基于LSTM(长短期记忆)网络的预测模型在预测15分钟前的均方根误差(RMSE)方面有较好的表现。这项研究可以作为未来研究技术和方法的基础,这些技术和方法可以在挪威气候的准确性和稳定性方面产生高性能的预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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