Aggregating the data value of multiple application scenarios: a case from electricity market

Electricity market data has diverse application scenarios, making it essential to aggregate value across these scenarios for accurate data evaluation. This paper establishes a multidimensional data value evaluation and aggregation model for data samples, incorporating carbon emission reduction and price forecasting scenarios. These values are based on load forecasting for emissions reduction and electricity price forecasting for power sales company profits. TMC algorithm is used to quickly compute the aggregated value, which is represented by the Shapley value, of data samples. A case study of 183 data samples of electricity market from April to October 2023 in Hebei Province, China, was conducted. The analysis considered three weighting situations, showing that the aggregated value of a single sample ranged from [-300, 300] thousand yuan. Under different situations, the weight allocation strongly influences the distribution of values, with samples from mid-June to late July showing higher aggregated values.