Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal day-ahead orders using stochastic programming and noise-driven
  RNNs   This paper presents a methodology for strategic day-ahead planning that uses
a combination of deep learning and optimization. A noise-driven recurrent
neural network structure is proposed for forecasting electricity prices and
local inflow to water reservoirs. The resulting forecasters generate
predictions with seasonal variation without relying on long input sequences.
This forecasting method is employed in a stochastic program formulation of the
day-ahead problem. This results in optimal order strategies for a price-taking
hydropower producer participating in the Nordic day-ahead market. Using an
open-source software framework for stochastic programming, the model is
implemented and distributed over multiple cores. The model is then solved in
parallel using a sampling-based algorithm. Tight confidence intervals around
the stochastic solution are provided, which show that the gain from adopting a
stochastic approach is statistically significant.

6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org