Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps

Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet

Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps

Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet

ProLoaF: Probabilistic load forecasting for power systems

Today, the energy supply does not follow the demand in a controlled manner anymore. Thus, forecasting the electricity consumption became essential for the operation of power systems. Already numerous open source software tools exist that provide forecasting models, which are configurable for different forecasting tasks. In the case of electrical energy demand, a change in the geographical or temporal settings, requires specific domain knowledge on relevant data and influencing factors that are to be considered when developing data-driven forecasting models. With ProLoaF, we propose a holistic machine-learning based forecasting project, which offers the developer a continuous deployment of reliable forecasts for the power system domain. ProLoaF serves for probabilistic forecasts of the electric energy consumption and non-controllable generation in future power system operation. By overlapping Machine Learning (ML), DevOps and power systems engineering disciplines, we aim to accelerate future forecasting model development by reducing consultation work between domain experts

Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels

Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case

Transparency and Involvement of the Energy-Related Industry in a Data Sharing Platform

The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partners