## 4 Conclusions  
In this investigation, the aim is to develop a lightweight model to determine the future electricity consumption of a residential building based on historical information. This study has identified that even three features can work well with an elaborate tuning of an outstanding ML model SVR producing 14% CV. We broaden our investigation to evaluate the influence of the number of inputs and outputs, which has not been done before. Specifically, the time-series model, which depends more on the periodicity of the data, cannot improve itself by effortlessly adding the number of inputs which sometimes is a helpful tactic for the non-time series models. The finding that more output can impede the model's performance will contribute to the area of multiple steps predictions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & \
\begin{tabular}{c} **Temporal** \\ **Granularity** \\ \end{tabular} & **Building** & **Features** & **Data Size** &
\begin{tabular}{c} **Best** \\ **Performance** \\ \end{tabular} \\ \hline
[38] & Hourly & \
\begin{tabular}{c} Cooling system \\ \end{tabular} & Eight features &
\begin{tabular}{c} 33,189 \\ samples \\ \end{tabular} & RMSE: 1.55 \\ \hline
[16] & Hourly & Commercial &
\begin{tabular}{c} Seven weather conditions \\ \end{tabular} & 27.5 months & R2: 0.95 \\ \hline
[17] & Hourly & Residential & 140 sensors & One year & CV: 20.05\% \\ \hline
[22] & \begin{tabular}{c} Sub-hourly \\ \end{tabular} & Residential & \begin{tabular}{c} Temperature, date, cosine of the hour, sine of the hour \\ the hour \\ \end{tabular} & 84 days & \begin{tabular}{c} CV: 14.88\% \\ \end{tabular} \\ \hline SVR—Our study & Sub-hourly & Residential & \
\begin{tabular}{c} Electricity consumption, Date and time \\ \end{tabular} & One year &
\begin{tabular}{c} CV: 14.25\% \\ R2: 0.95 \\ RMSE: 2.6210 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of the result. To allow the practical implications for the proposed model, only a smart meter is required to monitor and record necessary features. This approach will provide flexible energy management to meet the net-zero goal and reduce CO2 emissions. A limitation of this study is that the building studied is a student accommodation which reflects high mobility each year due to graduation. Every academic year, many new students will move to the accommodation, resulting in slightly different electricity usage patterns. The model learnt from the previous year can give some biased predictions on the varied new pattern. Therefore, there is a potential that the model needs to be tuned regularly to ensure high performance. The performance of the proposed model for a new academic year can be guaranteed as the main feature of the building resulting from the university's timetable will not change. In future works, we will examine ensemble models [39], as the three models discussed here operate separately. There is a possibility that an ensemble method assembling several models in parallel or series can enhance robustness and generalizability over a single model. Conceptualisation: S.K., J.H. and M.A. ; Investigation: S.K., J.H. and M.A. ; Methodology: S.K., J.H. and M.A. ; Data Analysis: J.H. and M.A. ; Validation: J.H., and M.A. ; Draft: J.H. and M.A. ; Review and Editing: [PERSON] and J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by H2020 MSCA, grant number 691135. The APC was funded by MDPI's Invited Paper Initiative. Not applicable. Data Availability Statement: Data can be made available upon request for collaboration. The authors are sincerely grateful to European Commission for the financial sponsorship of the H2020-MSCA-RISE Project No. 691135 \"RISEN: Rail Infrastructure Systems Engineering Network,\" which enables a global research network that tackles the grand challenge of railway infrastructure resilience and advanced sensing in extreme environments (www.risen2 rail.eu (accessed on 26 September 2022)) ([PERSON] et al., 2016).