Federated Deep Learning in Electricity Forecasting: An MCDM Approach

TL;DR

This paper introduces a multicriteria distributed deep learning approach using weighted goal programming for electricity demand forecasting, demonstrating improved performance over baseline models with dataset overlaps.

Contribution

It proposes a novel multicriteria ensemble method based on weighted goal programming for distributed deep learning in electricity forecasting.

Findings

Performance exceeds baseline models with dataset overlaps

Method is model and metric agnostic

Provides interpretable decision rules

Abstract

Large-scale data analysis is growing at an exponential rate as data proliferates in our societies. This abundance of data has the advantage of allowing the decision-maker to implement complex models in scenarios that were prohibitive before. At the same time, such an amount of data requires a distributed thinking approach. In fact, Deep Learning models require plenty of resources, and distributed training is needed. This paper presents a Multicriteria approach for distributed learning. Our approach uses the Weighted Goal Programming approach in its Chebyshev formulation to build an ensemble of decision rules that optimize aprioristically defined performance metrics. Such a formulation is beneficial because it is both model and metric agnostic and provides an interpretable output for the decision-maker. We test our approach by showing a practical application in electricity demand forecasting. Our results suggest that when we allow for dataset split overlapping, the performances of our methodology are consistently above the baseline model trained on the whole dataset.