2 research outputs found
Framework for collaborative intelligence in forecasting day-ahead electricity price
Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and
operational schedules. The decision making process is limited if no understanding is given on how and why
such electricity price points have been forecast. The present article proposes a novel framework that promotes
human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework
is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide
range of exogenous features, a combination of several time series decomposition methods and a collection of
time series characteristics based on signal processing and time series analysis methods. The model architecture
is supported by open-source automated machine learning platforms that provide a baseline reference used
for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a
human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed
at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework
has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean
absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI
[0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical
and numeric explanations that augments understanding on the model and its electricity price point forecasts
Framework for collaborative intelligence in forecasting day-ahead electricity price
Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and
operational schedules. The decision making process is limited if no understanding is given on how and why
such electricity price points have been forecast. The present article proposes a novel framework that promotes
human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework
is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide
range of exogenous features, a combination of several time series decomposition methods and a collection of
time series characteristics based on signal processing and time series analysis methods. The model architecture
is supported by open-source automated machine learning platforms that provide a baseline reference used
for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a
human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed
at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework
has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean
absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI
[0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical
and numeric explanations that augments understanding on the model and its electricity price point forecasts