Linear and neural dynamic models: shared benefits between the industrial customer and the ESCo from the energy services' perspective

In this corrspondence, we investigate the application of linear and neurodynamic models to solve the critical problem of obtaining the most cost-effective electrical load forecast for an industrial site. This is done from the perspective of an energy service company (ESCo). The ESCos' business model is based on the notion of making money by providing a means to enable their commercial customers to lower their electric bills. This in turn is based on the idea that the energy supplier's cost of doing business is reduced if its commercial customers can provide a reliable load forecast. A portion of that cost savings is passed on to the user as a discount in the selling price. Since it is not cost effective for the customer to install extensive monitoring instrumentation, the load forecast must be made on the basis of a model that is determined by applying systems identification techniques to the user's consumption pattern. By a single-point observation of the electrical energy consumption in a dairy plant at quarter-hour intervals over a two-year period, we were able to identify a model and use it to devise a straightforward strategy for nontrivial cost savings to the user and a profitable line of service for the ESCo. The performance of several models is compared. The results for the weekly-based models are reported and the effective cost reduction along with the implementation time to set up the service package are quantified and verified in the proposed final business plan. The project produces overall revenues of roughly euro13 600 over a five-year contracting period