Abstract: An embodiment models and predicts energy consumption and provides recurring and realistic opportunities to reduce energy consumption throughout the work day or process cycle using user interfaces to convey positive and negative feedback in a controlled manner; and user experience, that reward positive changes with increased positive feedback and reduced negative feedback. Energy consumption of categories of appliances, devices, and equipment is considered a random variable. Using archived energy data, business data, and other related data, statistical modeling is used to create inverse cumulative probability distribution functions. An energy budget (consumption prediction) is computed so that it meets a probability p of the budget being exceeded during a given interval. When the budget is exceeded the feedback is negative, otherwise feedback is positive. Each budget is computed as the value b of the random variable such that the probability that the random variable will be less than or equal to b is 1-p.

Abstract: An embodiment models and predicts energy consumption and provides recurring and realistic opportunities to reduce energy consumption throughout the work day or process cycle using user interfaces to convey positive and negative feedback in a controlled manner; and user experience, that reward positive changes with increased positive feedback and reduced negative feedback. Energy consumption of categories of appliances, devices, and equipment is considered a random variable. Using archived energy data, business data, and other related data, statistical modeling is used to create inverse cumulative probability distribution functions. An energy budget (consumption prediction) is computed so that it meets a probability p of the budget being exceeded during a given interval. When the budget is exceeded the feedback is negative, otherwise feedback is positive. Each budget is computed as the value b of the random variable such that the probability that the random variable will be less than or equal to b is 1-p.

Abstract: An embodiment models and predicts energy consumption and provides recurring and realistic opportunities to reduce energy consumption throughout the work day or process cycle using user interfaces to convey positive and negative feedback in a controlled manner; and user experiences that reward positive changes with increased positive feedback and reduced negative feedback. Energy consumption of categories of appliances, devices, and equipment is considered a random variable. Using archived energy data, business data, and other related data, statistical modeling is used to create inverse cumulative probability distribution functions. An energy budget (consumption prediction) is computed so that it meets a probability p of the budget being exceeded during a given interval. When the budget is exceeded the feedback is negative, otherwise feedback is positive. Each budget is computed as the value b of the random variable such that the probability that the random variable will be less than or equal to b is 1?p.