As solar power continues to grow and replace traditional energy sources, the
need for reliable forecasting models becomes increasingly important to ensure
the stability and efficiency of the grid. However, the management of these
models still needs to be improved, and new tools and technologies are required
to handle the deployment and control of solar facilities. This work introduces
a novel framework named Cloud-based Analysis and Integration for Data
Efficiency (CAIDE), designed for real-time monitoring, management, and
forecasting of solar irradiance sensor farms. CAIDE is designed to manage
multiple sensor farms simultaneously while improving predictive models in
real-time using well-grounded Modeling and Simulation (M&S) methodologies. The
framework leverages Model Based Systems Engineering (MBSE) and an Internet of
Things (IoT) infrastructure to support the deployment and analysis of solar
plants in dynamic environments. The system can adapt and re-train the model
when given incorrect results, ensuring that forecasts remain accurate and
up-to-date. Furthermore, CAIDE can be executed in sequential, parallel, and
distributed architectures, assuring scalability. The effectiveness of CAIDE is
demonstrated in a complex scenario composed of several solar irradiance sensor
farms connected to a centralized management system. Our results show that CAIDE
is scalable and effective in managing and forecasting solar power production
while improving the accuracy of predictive models in real time. The framework
has important implications for the deployment of solar plants and the future of
renewable energy sources.

CAIDE 是一个基于云技术的新框架，用于实时监测、管理和预测太阳辐照度传感器农场，通过模型和仿真方法，同时管理多个传感器农场并实时改进预测模型。该框架利用模型化系统工程和物联网基础设施，在动态环境中支持太阳能电站的部署和分析。实验结果表明，CAIDE 框架在管理和预测太阳能发电的同时，可以实时提高预测模型的准确性，对太阳能电站的部署和可再生能源具有重要意义。

太阳辐照传感器阵列的高级基于模拟的预测建模

Advanced simulation-based predictive modelling for solar irradiance  sensor farms

Cloud simulation environments today are largely employed to model and
simulate complex systems for remote accessibility and variable capacity
requirements. In this regard, scalability issues in Modeling and Simulation
(M\&S) computational requirements can be tackled through the elasticity of
on-demand Cloud deployment. However, implementing a high performance cloud M\&S
framework following these elastic principles is not a trivial task as
parallelizing and distributing existing architectures is challenging. Indeed,
both the parallel and distributed M\&S developments have evolved following
separate ways. Parallel solutions has always been focused on ad-hoc solutions,
while distributed approaches, on the other hand, have led to the definition of
standard distributed frameworks like the High Level Architecture (HLA) or
influenced the use of distributed technologies like the Message Passing
Interface (MPI). Only a few developments have been able to evolve with the
current resilience of computing hardware resources deployment, largely focused
on the implementation of Simulation as a Service (SaaS), albeit independently
of the parallel ad-hoc methods branch. In this paper, we present a unified
parallel and distributed M\&S architecture with enough flexibility to deploy
parallel and distributed simulations in the Cloud with a low effort, without
modifying the underlying model source code, and reaching important speedups
against the sequential simulation, especially in the parallel implementation.
Our framework is based on the Discrete Event System Specification (DEVS)
formalism. The performance of the parallel and distributed framework is tested
using the xDEVS M\&S tool, Application Programming Interface (API) and the
DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups
of $15.95\times$ and $1.84\times$, respectively.

本文提出了一种基于 DEVS 形式主义的统一并行和分布式 M＆S 架构，具有足够的灵活性，可以在云中部署并行和分布式模拟，而无需修改底层模型源代码，并在并行实现中获得最大 15.95 倍的加速比。

一种统一的云支持零散事件并行分布式仿真架构

A Unified Cloud-Enabled Discrete Event Parallel and Distributed Simulation Architecture

In the last decade, the Internet of Things paradigm has caused an exponential
increase in the number of connected devices. This trend brings the Internet
closer to everyday activities and enables data collection that can be used to
create and improve a great variety of services and applications. Despite its
great benefits, this paradigm also comes with several challenges. More powerful
storage and processing capabilities are required to service all these devices.
Additionally, the need to deploy and manage the infrastructure to efficiently
support these resources continues to pose a challenge. Modeling and simulation
can help to design and analyze these scenarios, providing flexible and powerful
mechanisms to study and compare different strategies and infrastructures. In
this scenario, Micro Data Centers (MDCs) can be used as an effective way of
reducing overwhelmed Cloud Data Center infrastructures. This paper explores an
M\&S methodology to study the overall power consumption of a healthcare IoT
scenario. The patients wear non-intrusive monitoring devices that periodically
generate tasks to be executed in MDCs. We extract the layout of existing urban
infrastructures, simulate the monitored population's behavior, and compare the
power consumption of several data center configurations.

本文论述了一种基于建模和仿真的方法，利用 Micro Data Centers（微型数据中心）来降低云数据中心面对的大量 IoT 设备带来的压力，并且从城市基础设施、人口行为等角度，研究了医疗保健 IoT 场景下的总能耗与多种数据中心配置之间的比较。