Claims
1. A computer-implemented method for evaluating the energy consumption in an industrial plant, comprising:

at a plurality of time instants, capturing with a plurality of sensors, sensor data from at least one level of the industrial plant, wherein said plurality of sensors comprises energy measuring sensors,

from said sensor data, digitally obtaining a plurality of energy consumption curves x_{j}, wherein j = 0, 1, 2, ..., J-1, J being a natural number, an energy consumption curve representing, along a certain time period T, discrete values of energy consumption corresponding to time intervals Δt into which said time period T is divided,

from said energy consumption curves x_{j}, applying a clustering algorithm for digitally computing a plurality of K energy consumption patterns C_{k}, wherein k = 0, 1, 2, ..., K-1, K being a natural number, K < J, wherein each energy consumption pattern C_{k} represents a set of energy consumption curves x_{j} grouped together according to a similarity metric, wherein each energy consumption pattern C_{k} comprises discrete values of energy consumption corresponding to said time intervals Δt into which said time period T is divided,

capturing data of the production achieved during said time period T at said at least one level of the industrial plant,

calculating, for each energy consumption curve belonging to each pattern, the aggregated sum of the discrete values of energy consumption during said time period T, thus obtaining the aggregated energy consumption for each energy consumption curve during said time period T,

digitally establishing a relationship between aggregated energy consumption for each energy consumption curve during said time period T and said captured data of production.

2. The method of claim 1, wherein stage of capturing with a plurality of sensors, sensor data from at least one level of the industrial plant, is done at periodic time instants separated at time intervals Δt.

3. The method of any preceding claim, wherein said at least one level of the industrial plant is at least one of industrial plant, line, process, machine or component of a machine.

4. The method of any preceding claim, wherein each energy consumption pattern C

_{k} is calculated as the mean value of each component x

_{ij} of each energy consumption curve x

_{j} belonging to said pattern.

5. The method of any preceding claim, wherein said clustering algorithm applies the following iterative process for minimizing the distance between elements forming a cluster and its corresponding pattern:

wherein x

_{ij} is the i-th component of the j-th energy consumption curve x

_{j}, C

_{ik}
^{t} is the i-th component of the k-th energy consumption pattern at time instant t, K is the total number of energy consumption patterns and I is the number of measurements within the whole time period,

wherein the optimum number K of patterns for the whole set of curves

x
_{j} ∀ j = 1,...,J is given by the point of maximum deflection in the representation of the variance between groups divided by the total variance of the selection from the set of curves, that is to say:

wherein VAR

_{in} is the variance between the elements of a group or cluster, and

wherein VAR

_{in} is defined:

wherein K is the total number of energy consumption patterns, that is to say, the total number of groups of energy consumption curves related by a similarity metric defined for a group, n

_{k} is the number of elements forming the k-th group or curves, C

_{k}
^{t} is the characteristic pattern for the k-th group of curves and

C^{t} is the mean pattern of the whole selection of energy consumption curves.

6. The method of any preceding claim, wherein for a new energy consumption curve obtained from sensor data at a new time period T, the degree of similarity/dissimilarity with respect to current patterns is evaluated by comparing the new energy consumption curve with the K patterns already defined and, if the new energy consumption curve mathematically fits in a previously defined pattern in terms of a similarity metric, it is determined that said new energy consumption curve belongs to said pattern in which the new energy consumption curve fits.

7. The method of claim 6, wherein if the new energy consumption curve mathematically fits in a previously defined pattern in terms of a similarity metric, the new energy consumption curve is digitally compared with all the energy consumption curves that belong to the cluster represented by the pattern, thus comparing the new curve with the curve of maximum energy consumption and with the curve of minimum energy consumption.

8. The method of either claim 6 or 7, wherein if the new energy consumption curve belongs to an existing energy consumption pattern, the new energy consumption curve is associated to the group of curves represented by said pattern and the pattern is updated taking into account the new energy consumption curve.

9. The method of claim 6, wherein if the new energy consumption curve does not fit in a previously defined pattern in terms of a similarity metric, determining if the new energy consumption curve represents an anomalous consumption or if it represents the appearance of a new type of energy consumption pattern.

10. The method of claim 9, wherein if it is determined that the new energy consumption curve represents an anomalous consumption, triggering an alarm associated to the energy consumption.

11. The method of any one of claims 6-10, further comprising:

capturing data of the production achieved during the same time period T of the new energy consumption curve,

digitally comparing the energy consumption and associated production during said time period of the new energy consumption curve, with the relationship energy versus production inferred from historic curves.

12. The method of claim 11, wherein if as a result of the digital comparison it is determined that there is energy inefficiency, triggering an alarm associated to the production.

13. The method of any one of claims 10-12, further comprising using sub-metering measurements in order to interpret the difference in energy consumption causing the anomalous consumption.

14. A system comprising processing means, the system being configured to perform the steps of the method of any one of claims 1-13.

15. A computer program product comprising computer program instructions/code for performing the method according to any one of claims 1-13, or a computer-readable memory/medium that stores program instructions/code for performing the method according to any one of of claims 1-13.