رویکرد منطق فازی برای پیش بینی تغییرات مصرف انرژی در سیستم تولید

This paper proposes an energy consumption change forecasting system using fuzzy logic to reduce the uncertainty, inconvenience and inefficiency resulting from variations in the production factors. The proposed fuzzy logic approach helps the manufacturer forecast the energy consumption change in the plant when certain production input factors are varied. Predictions given by the proposed system adopts the fuzzy rule reasoning mechanism so that any changes in the overall energy consumption will neither violate the stable power supply and production schedules nor result in energy wastage. To demonstrate how the fuzzy logic approach is applied to a manufacturing system, a case study of the energy consumption forecast in a clothing manufacturing plant has been conducted in an emulated environment. The result of the case indicates a percentage change in the plant’s energy consumption after analyzing three input parameters. This finding is able to provide a solid foundation on which decision makers and systems analysts can base suitable strategies for ensuring the efficiency and stability of a manufacturing system.

During the last few decades, the world’s oil supply has been either running short or fluctuating from time to time. Although technological progress during these years has been rapid, such as the development of alternative power sources, the huge demand for oil from almost all human-related daily activities is hardly ever satisfied. Such circumstances mean that higher and higher costs have to be paid for energy, which is often generated from oil. As a result, inevitably, high energy consumption industries continue to look for other sources of power supply in a more effective and efficient way. An accurate forecasting of energy consumption is critical to them, otherwise any large forecasting error would increase their burdens about operating cost (Rousselot, Balmat, & Gut, 1993). Manufacturing is one of the business sectors that is directly affected and so manufacturers are keen to implement cost-reduction strategies. The energy input of a manufacturing plant is enormous. It is disastrous and inconvenient for an operating manufacturing plant to run short of power. It is also an inefficient waste if the machines are kept operating during their idle periods. In their endeavours to find more and more efficient ways of consuming energy, manufacturers will closely examine the manufacturing process to try and find any step that contains room for improvement. They may also try to devise an overall production plan that can control the production system to run at its optimum level as well as stabilize the factory’s power supply. In order to devise such a plan, the plant requires a reliable approach to forecasting the energy consumption changes that take place during the manufacturing process. This study proposes the application of fuzzy logic approach to forecast the amount of energy consumption change when comparing it to a reference energy consumption level. Section 2 reviews some related studies of fuzzy logic theories and applications. The development of the proposed energy consumption change forecast system by fuzzy logic approach is represented in Section 3. In Section 4, a case example is used to demonstrate how the proposed fuzzy logic system is applied to a clothing manufacturing plant. With the present of inputs and results, a final change about the energy consumption can be drawn at the end of Section 4. Section 5 includes the concluding remarks and suggestions for further research studies.

In this paper, an energy consumption forecast system by the fuzzy logic approach is introduced for supporting the manufacturing plant’s operations. Using the illustration of a clothing manufacturing plant, it demonstrates how to apply the fuzzy logic system in dealing with uncertain situations vis-à-vis energy consumption during the operating process. Further research can be focused on following up ways to maintain a reliable power supply for a manufacturing system and the energy saving strategies in the manufacturing plants. These two possible follow-up studies can connect with the fuzzy energy consumption forecast system to form a more responsive, effective and convenient integrated decision support system for manufacturing plants. In general, the proposed system reduces the uncertainty to energy consumption which results in enhancing the stability and offers more room for cost-reduction during the manufacturing process.