Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances

EXA2PRO : A Framework for High Development Productivity on Heterogeneous Computing Systems

Programming upcoming exascale computing systems is expected to be a major challenge. New programming models are required to improve programmability, by hiding the complexity of these systems from application developers. The EXA2PRO programming framework aims at improving developers productivity for applications that target heterogeneous computing systems. It is based on advanced programming models and abstractions that encapsulate low-level platform-specific optimizations and it is supported by a runtime that handles application deployment on heterogeneous nodes. It supports a wide variety of platforms and accelerators (CPU, GPU, FPGA-based Data-Flow Engines), allowing developers to efficiently exploit heterogeneous computing systems, thus enabling more HPC applications to reach exascale computing. The EXA2PRO framework was evaluated using four HPC applications from different domains. By applying the EXA2PRO framework, the applications were automatically deployed and evaluated on a variety of computing architectures, enabling developers to obtain performance results on accelerators, test scalability on MPI clusters and productively investigate the degree by which each application can efficiently use different types of hardware resources.Funding Agencies|European Unions Horizon 2020 research and innovation programme [801015]; National Infrastructures for Research and Technology S.A. (GRNET) [SNIC 2020/13-113, SNIC 2016/5-6]; PRACE (Piz-Daint) [pr114]</p