Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

Mauro Castelli,Leonardo Trujillo,Leonardo Vanneschi

doi:10.1155/2015/971908

Abstract

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

Highlights

As reported in [1, 2], energy consumption forecasting (ECF) is the task of predicting the electricity demand on different time scales, in minutes, hours/days, months, and years
One of the outcomes of the European Energy Forecast conference [11] that took place in Brussels in February 2014 was the identification the following facts and open issues. (a) ECF will have a huge impact on economy in the near future. (b) ECF is a very difficult problem, since it is influenced by asynchronous and often unpredictable facts. (c) Several different geographical and time scales can be identified for ECF, which contribute to making the task even more complex. (d) The currently existing computational intelligence (CI) technologies
For all the considered Genetic Programming (GP) systems 30 runs have been performed. These figures clearly show that LSGP outperforms geometric semantic GP (GSGP) and standard GP (STGP) on both training and test sets, for both the considered error measures

Summary

Introduction

As reported in [1, 2], energy consumption forecasting (ECF) is the task of predicting the electricity demand on different time scales, in minutes (very short-term), hours/days (shortterm), months, and years (long-term). In a dynamic market environment, precise forecasting is the basis of electrical energy trade and spot price establishment for the system to gain the minimum electricity purchasing cost. With the amount of data steadily growing, the problem is getting more and more complex. All these facts show the importance of having reliable predictive models that can be used for an accurate energy consumption forecasting [2]. One of the outcomes of the European Energy Forecast conference [11] that took place in Brussels in February 2014 was the identification the following facts and open issues. (a) ECF will have a huge impact on economy in the near future. (b) ECF is a very difficult problem, since it is influenced by asynchronous and often unpredictable facts. (c) Several different geographical and time scales can be identified for ECF, which contribute to making the task even more complex. (d) The currently existing CI technologies

Methods

Results

Conclusion