Energy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

中文翻译：

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一种在分布式微电网中确保边缘级效率的可再生能源预测和控制方法

由于可再生能源 (RES) 为现代环境带来的好处，使用可再生能源 (RES) 进行的能源预测在研究领域中的权重正逐渐增加。使用可再生能源进行能源预测不仅有助于减轻温室效应，还有助于节约能源以备将来使用。多年来，已经提出了几种能源预测方法，所有这些方法都更关注预测模型的准确性，而很少或根本不考虑运行环境。然而，这项研究提出使用深度神经网络 (DNN) 在网络边缘的移动设备上进行能量预测。这确保了所有能源预测操作的低延迟和通信开销，因为它们是在网络外围执行的。尽管如此，通过为本地生成的数据提供永久存储和卸载超出本地移动设备能力的资源密集型计算以及为它们提供安全性的平台，云将用作对移动设备的支持。提出了电气网络拓扑，允许将多个 RES 无缝合并到分布式可再生能源 (D-RES) 网络中。此外，设计了一种新颖的电网控制算法，该算法使用预测模型对电网中的可再生能源 (RES) 进行协调有效的控制。用两个 RES 模拟电力网络，并使用 DNN 模型为模拟网络创建预测模型。该模型使用来自比利时太阳能发电公司 (elis) 的数据集进行训练，并使用不同数量的层进行试验，以确定执行预测操作的最佳架构。使用均方误差 (MSE) 和 r 平方来评估每种架构的性能。