# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 7 Results and Analysis
### Analysis of Proposed DSM Algorithm  
The results of the proposed DSM algorithm are shown in Figure 5. It is clearly seen that the load from peak hours are clipped and shifted to the off peak hours. The total power consumption, power supplied by the MG and power consumed from the SG are shown in Figure 5. The proposed DSM scheme was applied on the 24 h of 7 January 2017 because of the fairly reasonable wind power generation and no zero generation hour throughout the day that leads to a clear depiction of DSM results. The purpose of DSM is to reduce the consumption load of peak hours to minimize the usage of the dispatchable generators of SG. The MG only has WPP and no dispatchable generators. If the wind generation is insufficient, the MG purchase energy from SG. If energy demand of MG's consumers is in the peak hours, then the load of MG is shifted from peak hours to off peak hours. An assumption is made that the MG encourages its consumers to shift their load from peak hours to off peak hours by offering some incentives and consumers shift their consumption load, which leads to overall load shifting in MG; consequently, the consumption cost of consumers is reduced. MG gets the advantage of not purchasing more energy from SG in peak hours (where price is higher than off peak hours' price), which also leads to the purchasing cost reduction for MG. In this manner, the consumers will be satisfied and MG will have cost effective demand management. The proposed algorithm successfully shifts the load. In the proposed method, the load is shifted to off peak hours that are not late night. This is suitable because late night is sleeping hours, and the electricity cannot be consumed much. The goal of almost normally distributing the load profile is achieved. The load before DSM and after applying proposed DSM algorithm is shown in Figure 6. The load profile after DSM is more towards the normal distribution than the profile before DSM. The exact normal distribution of load cannot be achieved because of the fixed working hours. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**DM Score**} \\ \hline
**Season** & **EDCNN Compared to SELU CNN** & **SELU CNN Compared to CNN** & **EDCNN Compared to CNN** \\ \hline Spring DM-MAE & 1.4252 & 0.0842 & 1.4256 \\ Spring \(p\)-value & 0.0432 & 0.9242 & 0.1248 \\ Summer DM-MAE & 1.3622 & 0.1024 & 1.3692 \\ Summer \(p\)-value & 0.0326 & 0.8624 & 0.2142 \\ Autumn DM-MAE & 1.2714 & 0.1762 & 1.6728 \\ Autumn \(p\)-value & 0.0196 & 0.0242 & 0.9242 \\ Winter DM-MAE & 1.4632 & 1.1426 & 1.2464 \\ Winter \(p\)-value & 0.02762 & 0.9862 & 0.7642 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diebold–Mariano test results at a 95% confidence level and 5% significance level of \(p\)-value. Figure 5: Valley filling and peak clipping through Efficient DSM algorithm. The electricity consumption in working hours cannot be shifted to other hours in a manner to achieve perfectly normal distribution of load. A portion of load is able to be shifted, which is known as shift-able load. The goal is to shift the shift-able load to improve load factor and reduce price that is achieved by applying proposed DSM. Another goal of the proposed DSM algorithm is reducing the consumption cost. When the load is shifted to off peak hours, the consumption cost reduces due to the low power price in off peak hours. The reduction in consumption cost achieved by the proposed DSM algorithm is presented in Table 5, which shows the price before and after applying DSM algorithm. The cost reduced by DSM and its percentage is also mentioned. On average, 1.1% of total cost is reduced by applying the proposed DSM algorithm. When the proposed algorithm is applied to the 365 days of 1 2017, approximately $2.25 million consumption cost is reduced. The DSM results of one day consumption cost from all four seasons are presented in Table 5. One day from every season of the year is taken for calculating results of DSM algorithm, i.e., 1 January (winter), 1 April (spring), 1 July (summer) and 1 October (autumn).