Selecting right renewable energy resources (RESs) is emerging as a solution to alleviate energy crisis and environmental pollution. To better address the RESs selection problems, this paper proposes an enhanced Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS). Specifically, the decision information is characterized by linguistic terms and then encoded by interval type-2 fuzzy sets (IT2FSs). The current IT2FSs preprocessing models recklessly transform IT2FSs into crisp numbers, which may discount the superiority of applying fuzzy sets. Hence, we define a novel interval type-2 fuzzy projection model to measure the IT2FSs and some related theorems about the projection model are explored mathematically. Moreover, based on this new interval type-2 fuzzy projection model, an enhanced TOPSIS is proposed to calculate the closeness coefficients of alternatives. Of note, to keep information as much as possible, the obtained closeness coefficients are still IT2FSs. Finally, the Karnik–Mendel (KM) algorithms are employed to compare and rank those closeness coefficients. The effectiveness of the proposed method is demonstrated by a RESs selection case. Comparisons are also conducted to illustrate its advantages.

选择合适的可再生能源（RESs）可以缓解能源危机和环境污染。为了更好地解决RES的选择问题，本文提出了一种与理想解决方案相似的订单偏好增强技术。具体而言，决策信息以语言术语为特征，然后由区间2型模糊集（IT2FS）进行编码。当前的IT2FS预处理模型会不计后果地将IT2FS转换为清晰的数字，这可能会削弱应用模糊集的优越性。因此，我们定义了一种新颖的区间2型模糊投影模型来测量IT2FS，并在数学上探索了有关投影模型的一些相关定理。此外，基于此新的区间类型2模糊投影模型，提出了一种增强的TOPSIS算法来计算替代方案的接近系数。值得注意的是，为了尽可能多地保留信息，获得的接近度系数仍为IT2FS。最后，采用Karnik–Mendel（KM）算法对这些接近度系数进行比较和排名。RESs选择案例证明了该方法的有效性。还进行了比较以说明其优势。