The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.
Approximate solution for two stage open networks with Markov-modulated queues minimizing the state space explosion problem
