If you want to travel from here to Timbuktu efficiently (and therefore languorously) and are granted choice of :
- Model T Ford (16–25 mpg)
- F430 Ferrari (11–16 mpg)
It is clear that the car of preference is the Model T Ford - because it (can) travel further on one gallon of fuel.
But it may not.
A Model T sitting at traffic lights burns fuel slowly at 0mpg. In doing so it is less fuel efficient than the F430 with rubber burning and pedal crammed to the floor. Think about it benefit vs. fuel spent ! (maybe the F430 gets bonus for a "throaty" growl - but surely not for timeless class and industrial significance).
The point is obvious - energy efficiency depends greatly on how you use the tools you have and not only which tools you have (this makes a potential nonsense of many building benchmarking tools if they aren't applied to reality).
Supply side energy management is commodity management - how do we deliver kWh as efficiently as possible to the point of use, and at the time of use? It rather assumes an efficient market, and data fed back into that market (demand and supply elasticity) are all beneficial. Smart, however, it is not; any more than a stock-ticker moving across a trading desk is smart. The action of increasing demand, whether real or speculative increases price and volume supplied - and the market is pretty responsive. Old idea - not particularly smart!
Demand side energy management is the opposite of commodity management - a commodity is fungible, meaning that one "bit" of electricity or oil is very like another, and really you buy from the best value supplier (which basically means cheapest).
The perspective of the energy manager or building operator is different, it is reasonable that they may respond to energy prices rising by switching off least-essential use (ie they must differentiate on the basis of purpose). However, it is only reasonable to assume that they will if there is an element of price elasticity. That is if they are price sensitive.
The traditional concept of "Smart-Meters" is that there is some preference to save energy (motivation), and that the metering provides a flag to show a user how to. This assumes that either the user watches the "Smart-Meter" like a hawk (which may become tedious), or that control systems start to adjust use automatically verses price (a "smart thermostat") while being able to weigh up lost (or deferred) building services against the financial benefit of so doing. It sound pretty naive to assume this will happen any time soon in my opinion - not because we lack the technology, or motivation, but because we simply have not established values and metrics for comfort.
Again in my opinion, what may precede this is metering analysis at sufficient diagnostic resolution to know when expenditure is wholly wasted (like the Model T at the traffic lights).
If you look at consumption rates of a gas-fired boiler in the context of demand (the weather, time of day etc), it is largely obvious when it is "sitting at the traffic lights" - there is even a control term for the behaviour dry-cycling which occurs when under no load the boiler switches on and off rapidly (like an unused iron on an ironing board - Note simple smart iron idea !). This like many inefficiency patterns can be recognised by observing profiles of energy consumption and is the underlying approach at kWIQly.
If you look at consumption rates of a gas-fired boiler in the context of demand (the weather, time of day etc), it is largely obvious when it is "sitting at the traffic lights" - there is even a control term for the behaviour dry-cycling which occurs when under no load the boiler switches on and off rapidly (like an unused iron on an ironing board - Note simple smart iron idea !). This like many inefficiency patterns can be recognised by observing profiles of energy consumption and is the underlying approach at kWIQly.
So to conclude - So long as energy efficiency is about "Doing it for less", the first question should be "Why do it at all?" - If there is no good reason to "keep the engine running", turn it off. If we can diagnose problems remotely and in a scalable fashion from meter data only then energy saving is easy - Bring it on !