## 1 Introduction  
According to the United Kingdom (UK) Green Building Council, the UK is undergoing widespread climate changes attributed to greenhouse gas emissions, as most electricity is produced by foil fuels [1]. After 2010, a 30% emission reduction was seen mainly owing to the decarbonisation of the electricity grid [2]. However, the energy efficiency of buildings remains unresolved. Residential and commercial buildings account for around 40% of total building emissions in the United States [3]. Moreover, electricity demand for buildings has been increasing over the decades and forecasting electricity to match the supply and demand for buildings is essential because a large amount of electricity cannot be stored. Therefore, predicting future energy consumption has emerged as a powerful platform for demand and supply management which retrofits the energy efficiency of residential and commercial buildings. More importantly, the expansion of electricity efficiency alleviates fuel consumption and the emission of greenhouse gases. At the same time, energy consumption prediction guides renewable energy supply operators to provide enough renewable energy sources to match building energy demands. However, the foresight of electricity consumption can be operose since plenty of factors contribute to electricity consumption, such as the design of the buildings, weather conditions, and occupants' way of life [4]. In the present age, building energy consumption prediction and estimation has gained focus from researchers and industries. [PERSON] et al. extensively reviewed data-driven and physical modelling approaches predicting energy consumption [5]. The physical model is dedicated to using physical principles such as the design of buildings, operation, heating, ventilation, air conditioning (HVAC) equipment, and climate conditions to calculate the building's energy behaviour and thermal dynamics [6]. More in-depth calculations can be found in [7; 8; 9] for the physical model. The data-driven method, mainly focusing on machine learning methods, relies on acquiring energy usage patterns from historical data. This approach benefits from not diving into detailed calculations like the physical model. In recent years, there has been an increasing interest in this domain using Artificial Neural Network (ANN) [10], Support Vector Machine (SVM) [11], Long-Short Term Memory (LSTM) [12; 13], XGBoost [14], etc. This study proposes a data-driven method using historical and temporal dependency to predict electricity consumption. At the same time, this study also explores the effect of more outputs. A recent systematic literature review [5] summarised 63 pieces of literature predicting energy consumption in terms of algorithms, building types and features, etc. Researchers favoured using four categories of features, namely, weather conditions, conditions of buildings, time, and occupant behaviours [15]. Weather conditions refer to temperature, humidity, wind speed, etc., while conditions of buildings point to the indoor environmental conditions and designs of buildings. Time features are day types, such as a holiday or special event, and the type of hour. Occupant behaviour covers the number of occupants and building use schedule. The performance using these features can be good; however, it is not easy to implement as it needs more sensors to monitor the temperature or other weather indicators. The authors of [16] predicted the hourly energy consumption for the cooling system at a non-residential building using seven weather indicators like temperature, dew point temperature, wind speed etc. A good R\({}^{2}\) (0.71-0.95) was delivered for the 27.5 months of actual data. [PERSON] et al. have conducted two branches of tasks using 7 AI models, which were trained by a dataset collected by 140 different sensors in 1 year, acquiring the lowest CV of 20.15%.