• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.161-171
• /
• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• Journal of Bio-Environment Control
• /
• v.17 no.2
• /
• pp.90-95
• /
• 2008

It has become a big matter of concerns that the skill and measures against reduction of energy and cost for heating a protected horticultural greenhouse were prepared. But in these days necessity of cooling a protected horticultural greenhouse is on the rise from partial high value added farm products. In this study, therefore, a horizontal type geothermal heat pump system with 10 RT scale to heat and cool a protected horticultural greenhouse and be considered to be cheaper than a vertical type geothermal heat pump system was installed in greenhouse with area of $240\;m^2$. And cooling performances of this system were analysed. As condenser outlet temperature of heat transfer medium fluid rose from $40^{\circ}C$ to $58^{\circ}C$, power consumption of the heat pump was an upturn from 11.5 kW to 15 kW and high pressure rose from 1,617 kPa to 2,450 kPa. Cooling COP had the trend that the higher the ground temperature at 1.75 m went, the lower the COP went. The COP was 2.7 at ground temperature at 1.75 m depth of $25.5^{\circ}C$ and 2.0 at the temperature of $33.5^{\circ}C$ and the heat extraction rate from the greenhouse were 28.8 kW, 26.5 kW respectively at the same ground temperature range. 8 hours after the heat pump was operated, the temperature of ground at 60 cm and 150 cm depth buried a geothermal heat exchanger rose $14.3^{\circ}C$, $15.3^{\circ}C$ respectively, but the temperature of ground at the same depth not buried rose $2.4^{\circ}C$, $4.3^{\circ}C$ respectively. The temperature of heat transfer medium fluid fell $7.5^{\circ}C$ after the fluid passed through geothermal heat exchanger and the fluid rejected average 46 kW to the 1.5 m depth ground. It analyzed the geothermal heat exchanger rejected average 36.8 W/m of the geothermal heat exchanger. Fan coil units in the greenhouse extracted average 28.2 kW from the greenhouse air and the temperature of heat transfer medium fluid rose $4.2^{\circ}C$after the fluid passing through fan coil units. It was analyzed the accumulation energy of thermal storage thank was 321 MJ in 3 hours and the rejection energy of the tank was 313 MJ in 4 hours.

• Journal of Bio-Environment Control
• /
• v.22 no.2
• /
• pp.182-187
• /
• 2013

In closed plant production system like plant factory, changes in environmental factors should be identified for conducting efficient environmental control as well as predicting energy consumption. Since high relative humidity (RH) is essential for crop production in the plant factory, transpiration is closely related with RH and should be quantified. In this study, four varieties of lettuces (Lactuca sativa L.) were grown in a plant factory, and the leaf areas and transpiration rates of the plants according to DAT (day after transplanting) were measured. The coefficients of the simplified Penman-Monteith equation were calibrated in order to calculate the transpiration rate in the plant factory and the total amount of transpiration during cultivation period was predicted by simulation. The following model was used: $E_d=a*(1-e^{-k*LAI})*RAD_{in}+b*LAI*VPD_d$ (at daytime) and $E_n=b*LAI*VPD_n$ (at nighttime) for estimating transpiration of the lettuce in the plant factory. Leaf area and transpiration rate increased with DAT as exponential growth. Proportional relationship was obtained between leaf area and transpiration rate. Total amounts of transpiration of lettuces grown in plant factory could be obtained by the models with high $r^2$ values. The results indicated the simplified Penman-Monteith equation could be used to predict water requirements as well as heating and cooling loads required in plant factory system.