## 1 Introduction
### State of the Art of Day-Ahead Load Forecasting  
Many studies have been published on short-term and day-ahead load forecasting. Table 2 summarizes several significant publications, highlighting the technologies used and their key findings. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Forecast Horizon** & **Type of Forecast** & **Use of Load Demand Forecast** \\ \hline
1 s to several s & Real-time (RT) & Power system dynamic and transient analysis. Real-time monitoring. \\  & & Real-time scheduling of generation (RT electricity dispatch), load frequency control, resource dispatch, auction-based electricity markets, PV storage control. \\  & & Deregulated power, energy pricing. Intra-hour forecast: operating regulation reserves, storage system optimization, grid quality and stability. Islanding with high solar penetration. Unit commitment, economic dispatch, electricity market. Intra-day forecast: electricity trading, control of electric loads. Power system load scheduling, load balancing, automatic generation control (AGC), reserve planning and control. Day-ahead forecast: Unit commitment and utilities planning, market transactions. \\  & & Dominant analysis of the power units, scheduling of power systems maintenance. Scheduling the maintenance of power system elements, electricity pricing. Fuel supply strategies. \\
1 year to several years & Long-term & Power system planning, expansion and operation management. \\ \hline \end{tabular}
\end{table} 
Table 1: Forecast horizons, types of forecasts, and typical applications in electrical power systems. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Forecast Horizon** & **Data from** & **Tools** & **Features** \\ \hline [PERSON] et al., 2009 [17] & One hour ahead & California electricity market & Multilayer perceptron neural networks & Forecasting values are computed for test weeks in summer and winter, finding absolute percentage errors ranging from 3.29\% to 0.01\%. \\ \hline [PERSON] et al., 2015 [18] & One day ahead & Tunisian Power Company & Random forest & Information about the country’s weather or market is used according to expert knowledge. The forecasted values are calculated for weekdays and weekends, achieving a MAPE of less than 2.3\%. \\ \hline [PERSON] et al., 2016 [19] & One day ahead & Albania & Fuzzy logic & Load demand is forecasted for various days of test weeks during the seasons. \\ \hline [PERSON] et al., 2019 [20] & 24 h, 7 days, and 30 days & Abu Dhabi & LSTM neural network & Exogenous variables, such as temperature, humidity, and wind speed, are used to train the LSTM NN. MAPE values of 1.522\%, 2.16\%, 5.97\%, and 9.75\% are found for forecasting horizons of 24 h, 28 h, 7 days, and 30 days. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Forecast Horizon** & **Data from** & **Tools** & **Features** \\ \hline [PERSON] et al., 2009 [17] & One hour ahead & California electricity market & Multilayer perceptron neural networks & Forecasting values are computed for test weeks in summer and winter, finding absolute percentage errors ranging from 3.29\% to 0.01\%. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Forecasting values are computed for test weeks in summer and winter, finding absolute percentage errors ranging from 3.29\% to 0.01\%.** \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Forecasting values are computed for test weeks in summer and winter, finding absolute percentage errors ranging from 3.29\% to 0.01\%.** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of some of the relevant publications highlighting the technology utilized and their major findings.