Hierarchical Demand Forecasting Benchmark for the Distribution Grid
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and probabilistic forecasts. We also compare the ability of different hierarchical techniques in improving the bottom level forecasters' performances. Both the raw and cleaned datasets, including meteorological data, are made publicly available to provide a standard benchmark for evaluating forecasting algorithms for demand-side management applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Demand ForecastingManagementSimilar Papers 제목 키워드 기반
Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand
The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which req…
quantile regressionLarge Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarc…
Demand ForecastingTime SeriesTime Series ForecastingHierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models
Demand forecasting in competitive, uncertain business environments requires models that can integrate multiple evaluation perspectives rather than being restricted to hyperparameter optimization based on a single metric.…
Hyperparameter OptimizationEfficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method
Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face. A proven approach for addressing this challenge is to assess distribution grid adequ…
Computational EfficiencyDemand ForecastingFoundation Models for Demand Forecasting via Dual-Strategy Ensembling
Accurate demand forecasting is critical for supply chain optimization, yet remains difficult in practice due to hierarchical complexity, domain shifts, and evolving external factors. While recent foundation models offer …
Time Series Forecasting