  In electricity markets, retailers or brokers want to maximize profits by
allocating tariff profiles to end consumers. One of the objectives of such
demand response management is to incentivize the consumers to adjust their
consumption so that the overall electricity procurement in the wholesale
markets is minimized, e.g. it is desirable that consumers consume less during
peak hours when cost of procurement for brokers from wholesale markets are
high. We consider a greedy solution to maximize the overall profit for brokers
by optimal tariff profile allocation. This in-turn requires forecasting
electricity consumption for each user for all tariff profiles. This forecasting
problem is challenging compared to standard forecasting problems due to
following reasons: i. the number of possible combinations of hourly tariffs is
high and retailers may not have considered all combinations in the past
resulting in a biased set of tariff profiles tried in the past, ii. the
profiles allocated in the past to each user is typically based on certain
policy. These reasons violate the standard i.i.d. assumptions, as there is a
need to evaluate new tariff profiles on existing customers and historical data
is biased by the policies used in the past for tariff allocation. In this work,
we consider several scenarios for forecasting and optimization under these
conditions. We leverage the underlying structure of how consumers respond to
variable tariff rates by comparing tariffs across hours and shifting loads, and
propose suitable inductive biases in the design of deep neural network based
architectures for forecasting under such scenarios. More specifically, we
leverage attention mechanisms and permutation equivariant networks that allow
desirable processing of tariff profiles to learn tariff representations that
are insensitive to the biases in the data and still representative of the task.
