Multi-kernel anticipatory approach to intelligent control with application to load management of electrical appliances

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

doi:10.1109/med.2016.7536072

Cited by 8 publications

(5 citation statements)

References 20 publications

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Section: Methodsmentioning

confidence: 99%

“…In this work, the fuzzy rules have two conditions in the left-hand right side connected with the operator AND. With respect to fuzzy logic, the operator AND is numerically evaluated as the maximum of the two membership values where A(x) and B(x) are two fuzzy sets (or fuzzy statements, see [29]). In a fuzzy inference engine, it is common that more than one rule is fired, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…3). The goal of the fuzzy inference is to correlate the fuzzy sets of the inputs with the sets of the output variable [29]. The fuzzy rules that comprise the inference engine in the current work are given below IF MAPE is LOW, and Precision is HIGH, THEN Reproduction is HIGH IF MAPE is LOW, and Precision is MEDIUM, THEN Reproduction is MEDIUM IF MAPE is LOW, and Precision is LOW, THEN Reproduction is MEDIUM IF MAPE is MEDIUM, and Precision is HIGH, THEN Reproduction is LOW IF MAPE is MEDIUM, and Precision is MEDIUM, THEN Reproduction is MEDIUM IF MAPE is MEDIUM, and Precision is LOW, THEN Reproduction is MEDIUM IF MAPE is HIGH, and Precision is HIGH, THEN Reproduction is MEDIUM IF MAPE is HIGH, and Precision is MEDIUM, THEN Reproduction is LOW IF MAPE is HIGH, and Precision is LOW, THEN Reproduction is LOW …”

Section: Wind Speed Forecastingmentioning

confidence: 99%

“…In this work, the fuzzy rules have two conditions in the left‐hand right side connected with the operator AND. With respect to fuzzy logic, the operator AND is numerically evaluated as the maximum of the two membership values

normalA ()(, x) AND normalB ()(, x) = max ()(, μ_{A} ()(, x), μ_{B} ()(, x))

where A( x ) and B( x ) are two fuzzy sets (or fuzzy statements, see [29]).…”

Section: Wind Speed Forecastingmentioning

confidence: 99%

“…Their combination is made by using any of the analytical operations, namely, addition, multiplication, and exponential. For instance, a simple example of synthesising a kernel by adding two simpler ones is given below

k ()(, x_{1}, x_{2}) = k_{1} ()(, x_{1}, x_{2}) + k_{2} ()(, x_{1}, x_{2})

with

k_{1}

and

k_{2}

being two valid kernel functions [29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed

Alamaniotis

Karagiannis

2019

IET Renewable Power Generation

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Robust forecasting of wind speed values is a key element to effectively accommodate renewable generation from wind in smart power systems. However, the stochastic nature of wind and the uncertainties associated with it impose high challenge in its forecasting. A new method for forecasting wind speed in renewable energy generation is introduced in this study. The goal of the method is to provide a forecast in the form of an interval, which is determined by a mean value and the variance around the mean. In particular, the forecasting interval is produced according to a two-step process: in the first step, a set of individual kernel modelled Gaussian processes (GP) are utilised to provide a respective set of interval forecasts, i.e. mean and variance values, over the future values of the wind. In the second step, the individual forecasts are evaluated using a fuzzy driven multiplexer, which selects one of them. The final output of the methodology is a single interval that has been identified as the best among the GP models. The presented methodology is tested on the set of real-world data and benchmarked against the individual GPs as well as the autoregressive moving average model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%