# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 3 Results
### Load Reduction  
The developed system is tested under two conditions, without participation in ADR programs and with participation in ADR programs via OpenADR, to show its capability of executing load reduction of an air conditioner. Executing load reduction is the fundamental key for any demand response algorithm. Table 3 shows the specifications of the inverter air conditioner under test. Table 4 shows the testing date, starting time and ending time, outdoor temperature, indoor initial temperature, and AC temperature setting for the condition without participating in ADR programs. Two different temperature settings for the air conditioner (AC) (Cases 1 and 2) are tested for the condition without participating in ADR programs. Considering the scorching summer in Taiwan and energy savings, the Taiwan Power company suggests setting the AC temperature at around 26-28 \({}^{\circ}\)C. Therefore, the temperature is set at 26 \({}^{\circ}\)C for the worst scenario (consuming the most energy for a temperature setting around 26-28 \({}^{\circ}\)C while still able to save some energy consumption) in Case 2. To see the impact of the temperature setting increased by 1 \({}^{\circ}\)C, we assume that the user initially sets the temperature at 25 \({}^{\circ}\)C in Case 1 and use Case 1 as the base case; therefore, the temperature is also set at 25 \({}^{\circ}\)C in Cases A, B, and C. Since the optimal algorithm for the energy management system with ADR programs is not the main scope of our paper, some complicated factors, such as users' comfort, preferences, etc., are not considered in the test. The functionality of the developed DRC can be demonstrated by simply controlling the cooling/fanning duty cycle of the air conditioner. Three different duty cycles of cooling/fanning for controlling air conditioning (Cases A-C) are tested for participating in ADR programs: (1) 15 min for cooling mode and 15 min for fanning mode: duty cycle 0.5, (2) 30 min for cooling mode and 15 min for fanning mode: duty cycle 0.67, and (3) 45 min for cooling mode and 15 min for fanning mode: duty cycle 0.75. Table 5 shows the testing date (hot summer days), starting time and ending time, outdoor temperature, indoor initial temperature, durations for cooling and fanning modes, duty cycle, and the number of cycles in the testing duration for participating in ADR programs. Figure 13: Demand response controller physical modules. \begin{table}
\begin{tabular}{l l} \hline \hline
**Rated Voltage** & **Single Phase 220 V, 60 Hz** \\ \hline Rated cooling capacity & 8.0 kW \\ Cooling Seasonal Performance Factor (CSPF) & 5.12 \\ Energy Efficiency Ratio (EER) & 3.6 \\ Operating current & 11.2 A \\ Rated power & 2.22 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Air conditioner specifications. For each test case, after the VEN receives ADR messages and determines to conduct demand control, the DRC will send the control signals to the air conditioner at the starting time. The cooling/fanning mode of the AC will be controlled according to the control strategy (here, simply using the duty cycle). The duty cycle control strategy is like an on-off control. Within a control period, the duty cycle is defined as the ratio of the on-time to the control period (on-time + off-time). The cooling mode corresponds to the on-control, and the fanning mode corresponds to the off-control (actually, the AC is not off; the AC fan is still running). The power consumed during the cooling mode is more than during the fanning mode. Therefore, we can obtain different energy consumption by adjusting different duty cycles. Herein, the humidity and user satisfaction (comfort) are not considered to make the control strategy as simple as possible, showing the effectiveness of the proposed DRC. Once the experiment starts, the indoor temperature, voltage, and current are measured, and the voltage RMS (\(V_{rms}\)), current RMS (\(I_{rms}\)), average power per cycle (\(P_{ave,i}\)), and energy consumption (\(E\)) are calculated based on Equations (1)-(4). The sampling rate is 149 Hz.