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.

在建筑用电负荷数据采集过程中，不可避免地会引入不同种类的噪声，使观测值与实际值存在偏差，从而导致高度的不确定性。而这种不确定性使得短期建筑用电负荷难以实现准确的点预测。为提高预测结果的合理性，为决策者提供更有效的信息，本文提出了一种新的多目标算法优化模模糊方法，可实现短期电力负荷区间预测。第一的，通过用区间函数权重替换传统的基于 SIRMs 的模糊推理系统 (SIRM-FIS) 的原始函数权重，提出了一种新的基于单输入规则模块 (SIRMs) 的分布式区间模糊模型 (SIRM-DIFM) . 然后，提出了一种用于构建 SIRM-DIFM 的数据驱动学习方案。该学习方案包括两个主要步骤。第一步利用迭代最小二乘法为SIRM生成模糊规则并确定区间函数权重的中心，第二步采用基于遗传算法（GA）的多目标优化算法确定区间函数权重的宽度。通过这两个步骤，可以实现准确的点估计和合理的区间预测结果。最后，进行了两个建筑电力负荷预测实验来验证所提出的 SIRM-DIFM 的有效性。仿真结果表明，所提出的SIRM-DIFM可以弥补点估计精度低的缺点，预测区间可以有效覆盖观测数据，为决策者提供更可靠和有用的信息。