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Found 7 papers, 1 papers with code
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
Electricity Price Forecasting: The Dawn of Machine Learning
Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons.
Scenario-based Nonlinear Model Predictive Control for Building Heating Systems
In this article, we combine a stochastic scenario-based MPC (SBMPC) controller together with a nonlinear Modelica model that is able to provide a richer building description and to capture the dynamics of the building more accurately than linear models.
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms.
Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs
One of such factors is the network's structure.
Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e. g.~operational planning, power purchases, reserve activation, etc.
Forecasting day-ahead electricity prices in Europe: the importance of considering market integration
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance.
