An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic

Abstract

Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony optimization, genetic algorithm (GA) and fuzzy logic to construct a load forecasting expert system. The superiority and applicability of ACO-GA is shown for Iran's annual electricity load forecasting problem and results are compared with adaptive neuro-fuzzy inference system (ANFIS), which is a common approach in this field. The outcomes indicate that ACO-GA provides more accurate results than ANFIS approach. Moreover, the results of this study provide decision makers with an appropriate simulation tool to make more accurate forecasts on future electricity loads.