From 2d91c08910ac68d4db263a794538c32ccb2e8aa7 Mon Sep 17 00:00:00 2001
From: maximilian-hoffmann
 <45029954+maximilian-hoffmann@users.noreply.github.com>
Date: Thu, 9 Jun 2022 10:59:38 +0200
Subject: [PATCH] Update README.md

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 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index fd9093d..2368bb0 100644
--- a/README.md
+++ b/README.md
@@ -8,8 +8,8 @@ tsam is a python package which uses different machine learning algorithms for th
 tsam was originally designed for reducing the computational load for large-scale energy system optimization models by aggregating their input data, but is applicable for all types of time series, e.g., weather data, load data, both simultaneously or other arbitrary groups of time series.
 
 If you want to use tsam in a published work, **please kindly cite** one of our latest journal articles:
-* Hoffmann et al. (2021):\
-[**The Pareto-Optimal Temporal Aggregation of Energy System Models**](https://arxiv.org/abs/2111.12072)
+* Hoffmann et al. (2022):\
+[**The Pareto-Optimal Temporal Aggregation of Energy System Models**](https://www.sciencedirect.com/science/article/abs/pii/S0306261922004342)
 * Hoffmann et al. (2021):\
 [**Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models**](https://www.sciencedirect.com/science/article/abs/pii/S0306261921011545)