4,676 research outputs found
An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements
In this paper, we present a method to determine the global horizontal
irradiance (GHI) from the power measurements of one or more PV systems, located
in the same neighborhood. The method is completely unsupervised and is based on
a physical model of a PV plant. The precise assessment of solar irradiance is
pivotal for the forecast of the electric power generated by photovoltaic (PV)
plants. However, on-ground measurements are expensive and are generally not
performed for small and medium-sized PV plants. Satellite-based services
represent a valid alternative to on site measurements, but their space-time
resolution is limited. Results from two case studies located in Switzerland are
presented. The performance of the proposed method at assessing GHI is compared
with that of free and commercial satellite services. Our results show that the
presented method is generally better than satellite-based services, especially
at high temporal resolutions
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Operational solar forecasting for the real-time market
Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones
Optimal Rate of Direct Estimators in Systems of Ordinary Differential Equations Linear in Functions of the Parameters
Many processes in biology, chemistry, physics, medicine, and engineering are
modeled by a system of differential equations. Such a system is usually
characterized via unknown parameters and estimating their 'true' value is thus
required. In this paper we focus on the quite common systems for which the
derivatives of the states may be written as sums of products of a function of
the states and a function of the parameters.
For such a system linear in functions of the unknown parameters we present a
necessary and sufficient condition for identifiability of the parameters. We
develop an estimation approach that bypasses the heavy computational burden of
numerical integration and avoids the estimation of system states derivatives,
drawbacks from which many classic estimation methods suffer. We also suggest an
experimental design for which smoothing can be circumvented. The optimal rate
of the proposed estimators, i.e., their -consistency, is proved and
simulation results illustrate their excellent finite sample performance and
compare it to other estimation approaches
goSLP: Globally Optimized Superword Level Parallelism Framework
Modern microprocessors are equipped with single instruction multiple data
(SIMD) or vector instruction sets which allow compilers to exploit superword
level parallelism (SLP), a type of fine-grained parallelism. Current SLP
auto-vectorization techniques use heuristics to discover vectorization
opportunities in high-level language code. These heuristics are fragile, local
and typically only present one vectorization strategy that is either accepted
or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization
framework which solves the statement packing problem in a pairwise optimal
manner. Using an integer linear programming (ILP) solver, goSLP searches the
entire space of statement packing opportunities for a whole function at a time,
while limiting total compilation time to a few minutes. Furthermore, goSLP
optimally solves the vector permutation selection problem using dynamic
programming. We implemented goSLP in the LLVM compiler infrastructure,
achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp
and 4.07% on NAS benchmarks compared to LLVM's existing SLP auto-vectorizer.Comment: Published at OOPSLA 201
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