## 1 Introduction  
Building energy consumption contributes significantly to climate change [1]. Approximately 40% of the world's energy is consumed by the building sector [2]. Furthermore, significant population expansion has raised the need for energy in buildings [3]. Therefore, in order to hasten the shift to sustainable building methods, buildings must be energy efficient. Predictive analytics of building energy consumption are an effective method of achieving this energy efficiency [4]. Based on the insights provided by such predictive consumption, utility companies, consumers, and facility managers can take necessary stepsto make their buildings energy efficient [5]. Such efficiency is crucial to reduce energy costs and increase the energy security of buildings [6]. Moreover, energy prediction of buildings aids in the implementation of urban greening policies [7]. Meaningful prediction is offered by Artificial Intelligence (AI) models, such as machine learning and deep learning models, by discovering hidden pattern from historical energy consumption data [8]. These AI models learn from a large amount of labeled energy consumption data through supervised learning to offer such predictions [9]. However, obtaining labeled data for building energy consumption prediction is often challenging due to the high cost and effort involved in collecting accurate ground-truth measurements [10]. Many energy datasets lack sufficient labels because of sensor deployment limitations, data privacy concerns [11], and manual annotation burdens [10; 12]. This scarcity of supervised labeled data hinders the development of robust and scalable AI models for energy consumption prediction in diverse building environments. A labeled dataset can be imbalanced as well. Hence, using unlabeled energy data to train AI models can overcome the scarcity, inaccessibility, and imbalance issues of labeled energy data. Training an AI model with a small number of labeled data points and a large number of unlabeled data is called semi-supervised learning (SSL) [13; 14; 15; 16]. SSL learns knowledge from a small number of labeled data to meaningfully label a large amount of unlabeled data. Thus, SSL facilitates the training of an AI model by capitalizing on the abundance of unlabeled data, resulting in higher prediction accuracy [12]. SSL methods are divided into five groups [17]: (a) deep generative methods, (b) consistency regularization methods, (c) graph-based methods, (d) pseudo-labeling methods, and e) hybrid methods. Semi-supervised Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) are examples of deep generative methods. Although such methods generate new training examples, achieving optimal results for both generative task and downstream task is difficult. Virtual Adversarial Training (VAT) [16] is an example of consistency regularization methods. As a consistency-based SSL method, a novel Semi-supervised Universal Graph Representation Learning (SUGRL) framework was proposed by [18]. This framework performs graph classification in semi-supervised settings when graphs originate from diverse domains with heterogeneous structures. For this purpose, it learns domain-invariant representations through contrastive self-supervised learning and adversarial domain alignment. This method significantly improves generalization across domains using limited labeled data and abundant unlabeled graphs. Another consistency-based SSL method, Rationale-Informed Graph Neural Network (RIGNN), was proposed by [19]. This method identifies and leverages task-relevant subgraphs (rationales) to enhance classification robustness. By disentangling discriminative information from noisy or out-of-distribution data, RIGNN improves generalization under open-world and label-sparse settings. This work underscores the importance of interpretability and selective representation in advancing semi-supervised graph learning.