Abstract: A controller and/or a gateway acts as a feedback-based energy estimator for controlling initial and refined estimates of energy usage of one or more buildings controlled by the controller or gateway. An initial estimate of a building's energy needs for a specified time in the future (e.g., a month or a week ahead) are calculated, and then over time, in conjunction with an energy company, the initial estimate (and subsequent estimates) is revised based on the costs of the energy predicted or quoted by the energy company. The controller may examine jobs to be performed at the time as well as predictive information that may affect the building's energy needs (e.g., the predicted temperature for the time of the predicted energy needs). As expectations change (or as predicted factors such as temperature change), the energy company may be informed of the additional energy that will be needed (e.g., if the predicted temperature is increasing and cooling will be needed) or the excess energy that is expected (e.g.

Abstract: An energy usage coordinator controls the energy usage of individual buildings of a group (or portfolio) of buildings in one or more load zones. These buildings have all contracted with an energy company which controls (directly or indirectly) the energy usage coordinator. By agreeing to lower energy usage during times of peak usage (when the energy company may otherwise have to pay very high prices on the spot or short term market), the owners or managers of the buildings receive a preferential energy rate from the energy company. Such a preferential rate may be in the form of a fixed rate reduction or a variable rate reduction. Alternatively, the energy company may determine that the energy is currently selling for twice what its building portfolio has contracted to pay for it so it requests some portion of the portfolio to reduce usage so that it can sell the excess/saved energy in the market.

Abstract: Controllers associated with the high energy devices may are programmed or configured with processing rules by a building manager or an energy usage coordinator such that the controllers can respond to various changing conditions without having to be contacted by the energy usage controller. Such autonomous control allows the controllers to react more quickly to potentially take advantage of advantageous conditions and/or avoid disadvantageous conditions. For example, by tracking information from market information services and/or from historical, current and predicted information services, a controller may be programmed to reduce energy usage autonomously under a number of programmable conditions.

Abstract: An energy usage coordinator controls the energy usage of individual buildings of a group (or portfolio) of buildings in one or more load zones. These buildings have all contracted with an energy company which controls (directly or indirectly) the energy usage coordinator. By agreeing to lower energy usage during times of peak usage (when the energy company may otherwise have to pay very high prices on the spot or short term market), the owners or managers of the buildings receive a preferential energy rate from the energy company. Such a preferential rate may be in the form of a fixed rate reduction or a variable rate reduction. Alternatively, the energy company may determine that the energy is currently selling for twice what its building portfolio has contracted to pay for it so it requests some portion of the portfolio to reduce usage so that it can sell the excess/saved energy in the market.

Abstract: A controller and/or a gateway acts as a feedback-based energy estimator for controlling initial and refined estimates of energy usage of one or more buildings controlled by the controller or gateway. An initial estimate of a building's energy needs for a specified time in the future (e.g., a month or a week ahead) are calculated, and then over time, in conjunction with an energy company, the initial estimate (and subsequent estimates) is revised based on the costs of the energy predicted or quoted by the energy company. The controller may examine jobs to be performed at the time as well as predictive information that may affect the building's energy needs (e.g., the predicted temperature for the time of the predicted energy needs). As expectations change (or as predicted factors such as temperature change), the energy company may be informed of the additional energy that will be needed (e.g., if the predicted temperature is increasing and cooling will be needed) or the excess energy that is expected (e.g.

Abstract: A method of determining an optimal energy portfolio for a customer includes: quantifying the customer's risk/reward profile; obtaining customer data, including historical and forward customer data, said customer data including at least customer budgetary constraints; obtaining market data, including historical and forward market data; and determining, as the optimal energy portfolio for the customer, an energy portfolio based at least in part on (i) the customer's risk/reward profile, (ii) the customer's budget constraints, (iii) the customer data; and (iv) the market data.

Abstract: A Metering Billing and Information Solutions (“MBIS”) system for metering electricity usage and processing associated information includes an automated meter which electronically communicates metering information to a data processing system. The data processing system then appropriately calculates an electric bill using the metering information received from the meter. Additionally, the data processing system electronically stores the metering, billing, and other appropriate consumer account information. The MBIS system also includes an information system, which can be used by a consumer to remotely access the information stored by the data processing system. A payment system is also included in the MBIS system to facilitate electronic payment by a consumer.