Interval prediction of short-term building electrical load via a novel multi-objective optimized distributed fuzzy model.

In the process of building electrical load data collection, it is inevitable to introduce different kinds of noises, which makes the observation values deviate from the actual values, thus resulting in high levels of uncertainties. And such uncertainties make it difficult to achieve accurate point prediction of the short-term building electrical load. To improve the rationality of the prediction results and offer more effective information for decision makers, this paper proposes a novel multi-objective algorithm optimized modular fuzzy method which can accomplish the interval prediction for the short-term electrical load. First, one novel single-input-rule-modules (SIRMs)-based distributed interval fuzzy model (SIRM-DIFM) is proposed by replacing the original functional weights of the traditional SIRMs-based fuzzy inference system (SIRM-FIS) with the interval functional weights. Then, a data-driven learning scheme is presented for constructing the SIRM-DIFM. This learning sheme includes two main steps. The first step utilizes the iterative least square method to generate fuzzy rules for the SIRMs and determine the centers of the interval functional weights, while in the second step, the genetic algorithm (GA)-based multi-objective optimization algorithm is adopted to determine the widths of the interval functional weights. Through these two steps, accurate point estimation and reasonable interval prediction results can be achieved. Finally, two building electrical load prediction experiments are conducted to verify the effectiveness of the presented SIRM-DIFM. Simulation results indicate that the proposed SIRM-DIFM can compensate the shortcomings of the low accuracy of the point estimation and the predicted interval can effectively cover the observed data, providing the decision-makers more reliable and useful information. [ABSTRACT FROM AUTHOR]