Trading and Valuation of Day-Ahead Load Forecasts in an Ensemble Model

Abstract

Higher accurate load forecasts help the power system operator make better resource allocation and reduce operational costs. Ensemble learning has been widely used to improve the accuracy of final forecasts by combining multiple individual forecasts. In the digital economy era, the system operator can buy high-quality load forecasts from the data market and then combine them in an ensemble model to further enhance the quality of final forecasts. Consequently, the operator should share its operational profit (or reduced cost) fromforecasting improvement with forecast providers (agents). However, forecasts fromdifferent agents jointly affect the performance of the ensemble model, making it hard to quantify the contribution of each individual forecast. Even though several works have been done on the smart grid data market, there are very few works regarding energy forecast trading and valuation. To fill this gap, this paper builds up a novel framework for day-ahead load forecast trading and valuation in an ensemble model, which includes historical credit evaluation, data transaction, and payoff allocation. Specifically, three categories of payoff-allocating schemeswith distinct characteristics are proposed and compared in terms of applicable scope, computational complexity, and synergy consideration. Case studies on a real-world dataset illustrate how individual forecasts can be evaluated in an ensemble model.