Improving the Efficiency of Electricity Consumption by Applying Real-Time Fuzzy and Fractional Control

Abstract

Using energy more efficiently is one of the easiest ways to save money, reduce greenhouse gas emissions, and meet growing energy demands. Electricity consumption control is an emergent topic worldwide. The passive house idea is not new, but it is still actual and is discussed by researchers. This paper brings to the reader’s attention the combined use of fuzzy and fractional control methods to increase the performance of electricity consumption control, taking into account the current challenges in the energy field, together with a method for the automatic definition of fuzzy rules. In comparison with the no-control case, a 20% reduction in consumption is achieved with the present proposal. In the case of another control method, a 15% reduction was possible using Shakeri’s team’s method. The simulation of the proposed passive house control proves that it could ensure efficient electricity consumption that can be translated into electricity cost savings between 10 and 50 percent.

Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation

“…The cityblock distance [48], also known as the Manhattan distance, is a way to measure the distance between two points in a two-dimensional space (or higher dimensions). In the context of comparing two real-valued vectors, the cityblock distance is a way to measure the similarity or dissimilarity between two vectors based on the sum of the absolute differences between their corresponding elements.…”