## 1 Introduction
At the same time, they also trained the same model with another 6-months' data (including solar flux, temperature, cosine of the hour, sine of the hour, and date) from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) achieving a better CV of 2.71% than the model using 1-year dataset [17]. It is observable from [17] that a large number of features (150 sensory data) is sometimes not helpful compared to the dataset with five features. Long-term energy prediction has also gained interest where [18; 19] employed a variety of features, mainly from the designs of the buildings and the weather conditions to train models to envisage the overall energy consumption for residential buildings next year. However, the yearly prediction is beyond the scope as a short-term prediction is favourable to achieving more live assistance for the suppliers adapting their strategy. To predict future energy usage, the type of building is also critical as commercial buildings present a more consistent pattern than residential buildings. The commercial building is subjected to a pre-defined schedule, but the residential building is confounded by a more changeable time plan. Most of the non-residential based models provided better performance than the residential-based model, such as the non-residential models from [PERSON] et al. [20] and [PERSON] et al. [21], compared to the residential-based models in [17; 22]. Compared to all the literature above, our novelty refers to using as few features as possible (the least number of features used by [17] is five so far) to deliver lightweight ML models and less computational cost. Moreimportantly, this paper explores the multi-output case that allows delicate management. To the best of our knowledge, very little about the multi-output is currently known in this domain. Specifically, the multi-output contributes to predictions every time granularity in the following hours, unlike a long-term prediction with an overall value. In this study, electricity consumption and day are the only two types of attributes. Weather data are not employed to alleviate the tedium caused by acquiring data and the computational cost abatement. The focal point of this article is the inherent pattern of electricity consumption demonstrated in Figure 1 instead of factors affecting consumption. Figure 1 exemplifies an example using features at four previous timestamps to predict the energy consumption at the future two timestamps. Day feature is converted to Day sin and Day cos, which, to some extent, can reflect the pattern of temperature variation, such as night-time is cooler than daytime. Machine Learning (ML) is adopted in this research to avoid complex calculations but maintain satisfying performance. The contributions of this study can be summarised in the following two points:  
1. The study confirms the model's performance in the absence of weather features which makes the model less computational cost. It is almost effortless to deploy from an engineering prospect since no sensors for weather conditions are needed;
2. The multi-output allows a detailed scenario to respond to the electricity usage in the following hours. The remainder of the paper is organised as follows: Section 2 unveils the overview of the whole process from raw data collection and the development of ML models. Section 2 lays out data collection, analysis, and data pre-processing for ML models. Subsequently, details of three different ML algorithms, hyperparameter tuning, and evaluation criteria are discussed in the rest subsections of Section 2. Section 3 provides results for predicting the electricity usage in the next 30 min and discussions for a comparison with other research. Section 4 provides conclusions, limitations, and future directions.