1 Abstract—This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short- term load forecasting for the different modeled consumption processes. I. INTRODUCTION With the continuously growing demand of the energy, it is getting more important to develop systems capable to optimize the energy use. Energy management is nowadays a subject of great importance because of the facing problems of the global warming and oil shortage. In the industrial sector, the energy management systems have focused so far on the monitoring and off-line management of energy as it is outlined in (1). The typical energy management systems are based on the collection of information about the plant's operation using energy meters. Those systems help to monitor the operation of the installations, collect data and generate reports to identify the possible critical points of the consumptions. However, intelligent systems can improve the operation of the energy management systems, offering further functionalities such as predictive maintenance, energy optimization, fault diagnosis and energy forecast. Different approaches for energy savings and energy prediction have been studied over the past years. Evolutionary algorithms such as Particle Swarms (PSO), Gravitational Search Algorithms (GSA) and Simulated Annealing (SA) have successfully implemented in optimization and control applications (2). An implementation of a model based on Artificial Neural Network (ANN) was presented in (3-4) and (5) in order to estimate the load forecast in an electrical distribution system while in (6) a comparison between ANN and Fuzzy Logic is made on applications of short-term and medium-term load forecasting. An application of Neuronal Networks (NN) is presented in (7) in which it faces Multi-Input-Multi-Output (MIMO) applications with single input and output (SISO) net works. An ANFIS implementation for energy prediction of regional electrical loads in Taiwan was presented in (8), comparing its performance with other similar techniques (i.e., regression models, ANN-based models, Genetic algorithms and hybrid ellipsoidal fuzzy systems). A cellular multi-grid genetic algorithm is presented in (9) to face balancing problems in assembling lines. Techniques based on cultural algorithms are presented in (10) to resolve complex mechanical design optimization problems in an efficient and effective method. Thi s document presents the modeling and prediction algorithms that were developed in order to generate customizable mathematical models for different consumptions, as a way to improve the operation of a general energy monitoring system. The paper is organized as follows: section II describes an overview of the algorithm that has been used for the model's training and the energy forecast while section III outlines a brief explanation about the Genetic Algorithm's operation. In section IV, the proposed methodology is presented explaining the combination of the two algorithms in order to develop a system capable to train the consumption models autonomously. In Section V, the implementation of the system in the pilot plant is explained, presenting the different results that have been obtained during the test and the evaluation of the system. Finally, section VI summarizes the paper and discusses the different conclusions.
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Open Access
Sep 24, 2016
Andrew R Sommerlot + 3
