Long-Term Energy Consumption Forecast for a Commercial Virtual Power Plant Using a Hybrid K-means and Linear Regression Algorithm

Abstract

With regard to the development of a commercial Virtual Power Plant (VPP) – whose objective is to aggregate consumer and generator units that receive contractual benefits through a joint operation –, arises the necessity to implement a long-term energy consumption forecast algorithm, with the competence to provide inputs for the decision on the purchase or sale of long-term energy contracts. To perform this forecast, a hybrid algorithm with k-means clustering is used to cluster seasonal patterns of daily energy consumption through unsupervised machine learning, also applying regression concepts to identify trends and compose forecasted consumption. The model traces daily consumption profiles throughout the year utilizing measurement data to forecast the monthly energy consumption, which is segmented in peak and off-peak periods, in virtue of additional taxes that are charged for distributors of electricity in high demand hours. The proposed forecast model resulted in elevated accuracy in the aggregated loads context – which is the main objective of the VPP application –, increasing the usefulness of the VPP application as a decision-making tool for retailers, power distribution companies and other purposes involving grouping of electricity consumption.