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Dzud disasters, which cause livestock mortality during winter, frequently occur in Mongolia. To address this challenge, a solar freezing system was developed for rural areas with unreliable electricity. This system allows Mongolians to store meat in frozen storage during the fall, when prices are low, and sell it in the spring when prices are higher. Although rural Mongolia generally has a stable power supply, frequent outages necessitate a freezing system capable of operating under such conditions. The proposed solar freezing system utilizes solar power and the cold storage effect to maintain meat temperatures below 18◦C, a significant improvement over existing solar-powered systems, which can only maintain temperatures above 0◦C. Mongolia's dry, highland climate makes it particularly well-suited for such applications. A meat storage unit with improved insulation was tested to minimize reliance on regular grid power. Additionally, a simulator was developed to analyze system performance outside the winter period when the experiment was conducted. The results confirmed that the solar freezing system could operate continuously during winter with minimal reliance on grid power, provided it was turned off at night. The cold storage effect of the meat, combined with enhanced insulation, effectively prevented temperature increases. The solar freezing system reduced initial investment and maintenance costs by eliminating the need for batteries. This system will be paid back in 8.2 years using the Net Present Value (NPV) analysis, assuming no interest is charged. Thus, a freezing system for long-term meat storage during the Mongolian winter was successfully developed. Furthermore, simulation results indicated that meat storage using low-cost late-night electricity during summer nights in Mongolia is also feasible.

Due to the favorable condition of arid and cold climates for ever-increasing photovoltaic installations by supporting them to operate around their maximum power, it would be interesting to evaluate the solar potential of this climate. In this study, we proposed a simple, semi-empirical model to estimate the global horizontal irradiance (GHI) from the high-resolution visible channel satellite data provided by the Japanese meteorological satellite Himawari 8/9. The site adaptation procedure uses approximately 2–3 years of data recorded at four ground stations in Mongolia’s arid and cold regions to optimize the model parameters in a lookup table. Then, the model’s performance is evaluated using the independent test data of 1–2 years. The previous version of the proposed model and shortwave radiation product retrieved from the JAXA’s P-Tree system are also used for benchmarking as baselines. As a result, we found that the performance of the proposed model under a time granularity of 10 min surpassed them with an RMSE of 85 W/m2 in an arid desert to 114 W/m2 in a cold climate. A significant improvement was especially noticed in the capital city of Ulaanbaatar, where the resulting RMSE was 13 W/m2 and 131 W/m2 lower than the baseline models.

When large-scale photovoltaic generation facilities are online, it becomes difficult to maintain the normal operation of the power grid system owing to the stochastic nature of solar irradiation. However, highly accurate solar forecasting lowers the uncertainty, thus supporting the reliable and economically beneficial functioning of the utility grid. The time horizon of 24 hours is of particular interest since the electric grid operation is planned with a focus on a day-ahead market. Therefore, we propose an hourly day-ahead solar irradiance prediction framework based on deep-learning, which has continuously shown promising results in time series forecasting. The global horizontal irradiance is measured for 3-5 years at 4 sites in the arid and cold climate of Mongolia, as shown in the figure. We preprocessed the collected data by filling in missing values with clear sky irradiance calculated by the pvlib library. For the data split, Ulaanbaatar and Erdenet sites are used for training, while Choir site is used for validation. Finally, Darkhan site is reserved for testing considering its similarity to Erdenet, characterized by a cold and dry winter. Since the model was exposed to this climate during the training, we can expect improved performance on test data. In short, the training, validation, and test data make up 58%, 23%, and 19% of the overall dataset. Once data preprocessing is completed, various deep-learning structures are benchmarked against the traditional statistical models in terms of mean absolute error (MAE) and root mean squared error (RMSE). Namely, naïve forecasting, linear regression, dense neural network (DNN), recurrent neural network (RNN), long-short term memory (LSTM), and convolutional neural network (CNN) are considered. On the one hand, the MAE and RMSE of the simple naïve forecast were 34.42 and 101.68 W/m2, respectively, which increased/decreased to 42.84 and 97.56 W/m2 for the multivariate regression. On the other hand, both error metrics are significantly reduced when deep learning models are implemented. Out of total 6 models, the best-performing model is found to be the sequence-to-sequence RNN model with 2 layers, 100 neurons, optimized by the stochastic gradient descent (SGD) under a learning rate of 1e-3. The resulting MAE and RMSE were 38.46 and 81.13 W/m2, respectively.

Accurate nowcasting of the global horizontal irradiance (GHI) would allow real-time monitoring of photovoltaic (PV) generation. Thus, it would have a positive impact on electricity dispatch by reducing the amount of optimal reserves. In this study, we aim to nowcast an hourly GHI considering its importance in the economically beneficial operation of the power utility. Figure 1 illustrates the proposed, deep-learning-based solar estimation framework. The multimodal input dataset is recorded at 4 ground sites for 3-5 years. It consists of various meteorological parameters and satellite data provided by the Japanese geostationary meteorological satellite Himawari 8/9, resulting in a total of 39 input features. In the data preprocessing routine, the original temporal resolution of 10 minutes is averaged into hourly values. Furthermore, the data is partitioned into the training, validation, and test sets, constituting 58%, 23%, and 19% of total data, respectively. In other words, we train the model with data from 2 ground sites. During the training, the model updates its internal weights as it learns the solar characteristics. Then, the model is validated at another site to check if it is actually learning or just memorizing the training data. Finally, the learned model’s generalization capability would be tested on independent data from the remaining site.

Electrochemical reduction of CO2 is attracting attention as a CO2 conversion method which can use renewable energy on a large scale. Silver (Ag) is a well-known electrocatalyst for carbon monoxide (CO) production through CO2 reduction. CO2 reduction with Ag-based electrocatalysts has been mainly studied using H-type cell under CO2 ambient. However, the reaction rate has been limited due to the low solubility of CO2 to water. Recently, there are many reports on gas diffusion electrode (GDE) as cathode for CO2 reduction because it can increase current density largely, but the number of reports on Ag-based GDE is limited. Here, we assess the CO2 reduction activity of Ag electrocatalysts loaded on GDE and evaluated its product selectivity and durability using on-line gas analysis system. In addition, nano-porous Ag was also synthesized and tested, because nanostructured Ag is known to have higher CO2 reduction activity in comparison with bulk Ag owing to its high surface area.

Performance Evaluation of the Semi-empirical Solar Estimation Model based on Satellite Data enhanced with Ground Measurement

In Mongolia, four seasonal greenhouses are essential for food safety, as one of the main goals of sustainable development. However, operating four seasonal greenhouses in extremely cold weather is not an easy task, which requires additional heating. In cold regions like the USA, Canada and northern China, which have similar cold winters like Mongolia, there has been successful utilization of passive solar greenhouse with soil heat storage for more than two decades[1][2]. Our Renewable Energy Laboratory of National University of Mongolia conducted research on passive solar greenhouses with soil heat storage. The greenhouse is built in the Yargait mountain region of Ulaanbaatar with a wide transparent 6mm polycarbonate window and with a soil thermal heat storage. The greenhouse is 6 meters wide and 10 meters long, and the roof is made of 10cm thick EPS foam sandwiched with a 1mm metal sheet and the walls are constructed with a masonry block insulated with 10cm thick EPS foam. The excessive hot air from the greenhouse is sucked through the PVC pipe into the soil heat storage, which is 3m deep, 6m wide, and 10 m long and insulated with 10 cm thick EPS foam board. PVC pipes are buried within the soil, for circulating air mass in the soil to exchange heat. The stored heat in the ground is released during the night when there are no sun rays to heat up. Generally there are two types of seasonal thermal energy storage in the ground, high temperature ground storage 40℃~80℃ and low temperature ground storage 0℃~40℃ [3]. Our research object belongs to the low temperature ground storage type. The test has been performed under extreme cold winter condition at -25℃~-35℃ and the passive solar greenhouse revealed it’s capability to heat internal air using only the solar energy during the day and storing excessive heat into the soil. The soil heat storage heated up cold air by around +10℃ at best, and +5℃ at worst case. From this research, we conclude that a passive solar greenhouse can heat up only using solar energy during winter the day. On the other hand, the soil can be used as a thermal mass for storing large amounts of heat, recovering heat during the day, and releases at night. But, continuous cloudy days and extremely cold nights under -30 degrees can drain out the stored heat dramatically. Thicker insulation is needed to avoid heat loss dramatically.

In Mongolia, the building sector significantly contributes to the final energy consumption, particularly during harsh cold winters. The purpose of this study is to evaluate the impact of different air tightness levels on energy consumption. A case study was conducted on a residential house, with a to-tal area of 246 m2, using the Passive House Planning Package (PHPP) tool. The house's airtightness was measured twice using the single Blower Door standard measurement system, and a sensitivity analysis was performed to assess the variations in energy demand. The results reveal that improving air tightness from an initial air change rate of n50=2.96 to n50=1.77 led to a 26% reduction in heating energy demand, from 58.1 kWh/m2 to 43.2 kWh/m2 per year. Sensitivity analysis demonstrated that varying air tightness levels had a significant impact on energy demand, with higher air change rates resulting in increased energy consumption. This study highlights the importance of addressing building air tightness in Mongolian climates to optimize energy efficiency and reduce energy consumption in residential houses.

Air pollution caused by heating stoves has been the main source of critical health issues in Ulaanbaatar, Mongolia. The city experiences an annual average temperature of negative1.3°C and has short, warm summers while winters are bitterly cold and dry. Temperatures in January are as low as −40 °C. Although the government implements raw coal ban in the city to reduce air pollution since 2018, PM2.5 level exceeds 300ppm which lead the Air Quality Index to rank worst in the world. Since 2000, the government of Mongolia has been closely working on the issue and spent much of public money to reduce air pollution without success. Most of people think that to survive the coldest winters, they must burn something. This is why most people agrees to change stoves and improve coal quality, even though the air quality is remains worsening by doing so. To enlighten people’s vision Mongolian Passive House Institute and NUM has been working to showcase passive houses, as it requires almost no heating during winter. However, we needed more public attention to drastically change people’s mind. Before to take any action, research and experiments has been conducted to ensure the goldfish are not in any danger during the demonstration. We have simulated and tested thoroughly before we put real goldfish in the mockup. Design of the house followed basic principles of passive house. In addition, a thermal shutter has applied to the window. To reduce heat loss, the window functioned as door for the fish tank for the maintenance, wooden frame as shown below picture has constructed first and filled with shredded cardboard insulation(cellulose). Fish tank acts as thermal mass to absorb the heat from the sun. Roof top solar panels of 200W can provide enough power to run air pumps, LED lights and measuring devices inside. Thickness of cellulose insulation was 300mm for roof, walls, floor and additional 50mm XPS placed on internal surface. Thermal shutter made of 50mm XPS. Triple pane low-E glazing U-installed=1.24W/m2/K, G value 0.45, Solar irradiation 3000Wh/m2/day, internal T=18C, Ambient T=-25C(15h/24h) Td=-11C(9h/24h). Separate calculation on night and daytime and combined. This experiment conducted during 21Jan-1 Feb 2022. Outdoor temperature was -25C at night and -11C during day, while internal temperature was stable at 15-20C throughout 24h measurement, good correlation with the calculation. Broadcast TV and social media covered this activity during the experiment. Goldfish are healthy and alive, still performing the showcase on the streets of Ulaanbaatar to raise the awareness of energy efficiency to fight against air pollution. Because it covered very well on social media, many people are now considering insulating their homes and offices. Passive house movement has spread wide since then, workshops and training courses take place regularly to spread the idea of insulation. It clearly showed the public that we can immediately take out coal burning stoves after retrofitting our homes properly. Solar power can be the only source of energy to provide comfortable living conditions without the need to burn fossil fuels is the key take out of this project and it achieved this goal.

Good airtightness is essential to realize energy-efficient buildings. However, the issue of building airtightness has not been widespread yet in Mongolia. This study intends to evaluate the airtightness performance of single-family residential houses in Ulaanbaatar. The normalized test ISO 9972 (Thermal performance of buildings - Determination of air permeability of buildings - Fan pressurization method), known worldwide as the “Blower door test” is used to assess airtightness. This paper presents the results of a blower door test conducted in 6 houses that have different construction types. The measured air-change rate (n50) fluctuated from 0.21 to 6.51 ac/h at a 50 Pa pressure difference between the indoor and outdoor environment of the tested houses. The typical air leakage places happen at the frames of the window and socket.

In the harsh cold climate of Mongolia, there are many difficulties of deploying and putting use solar heating collector technology. One of them is developing control devices suitable to work in Mongolian conditions. Commercial solar thermal collector control devices are too complex and have several modes and settings to configure, which creates difficulties for users. In an extreme climate like Mongolia, it is necessary to change the modes and settings of control devices depending on the seasons. Therefore, it’s complicated to use a collector system for users. This research work will introduce the creation and development of an easy-to-use control device that is suitable for the unique characteristics of Mongolia’s climate and the actual need for heating and domestic hot water. The device is designed to be user-friendly and easy to configure and has a wireless connection to monitor and control from a far distance.

We aim to estimate solar resource of Mongolia using satellite data in combination with limited ground measurements. Visible channel images provided by Japanese Geostationary Meteorological Satellite (GMS) Himawari 8 are correlated with ground-based measurements of solar irradiation to derive parameters of the semi-physical model.

We aim to estimate solar resource of Mongolia using satellite data in combination with limited ground measurements. Visible channel images provided by Japanese Geostationary Meteorological Satellite (GMS) Himawari 8 are correlated with ground-based measurements of solar irradiation to derive parameters of the semi-physical model.

In this paper, we estimated diffuse fraction of global horizontal irradiation by implementing Erb’s model which computes diffuse fraction empirically from clearness index. At 4 locations across Mongolia, hourly, daily and monthly diffuse horizontal irradiation (DHI) values are estimated for year-long period and compared with the corresponding ground truth measurements. The results are demonstrated by different statistical parameters where normalized mean bias error (nMBE) and normalized root mean square error (nRMSE) were 2-20 and 10-33 percent, respectively. At all locations, the model underestimates diffuse irradiance which leads to negative bias and the correlation coefficients were greater than 0.87.

There are about 200,000 households living in detached houses and gers (yurts) with small coal stoves that burn raw coal in Ulaanbaatar city. A proper heating system and improvement of the energy efficiency of residential dwellings are vitally important for Ulaanbaatar city to reduce air pollution as well as for the operation of the current central energy system. This study shows the experimental results for two gers with two different heating systems and different thermal insulation, for investigating the merits of each. The technical feasibility of the system consisting of electric thermal storage (ETS) heater with a daytime charging schedule and areal photovoltaic (PV) system was also examined by using a simulation with software developed in MATLAB (Version, Manufacturer, City, State abbreviation if USA, Country). As a result of the experiment, the indoor comfort level and energy efficiency of the ger with added insulation and an ETS heater with nighttime charging were shown to be enhanced compared with those of the reference ger. The ger with added insulation and the ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation showed that the PV self-consumption rate is 76% for the Ger 2 with the ETS heater because of the daytime charging of ETS heater. The PV system supplied 31% of the total energy consumed, with the remaining 69% from the main grid.

In this study, a TraNsient SYStems (TRNSYS) simulation model for solar thermal systems is developed to assess the potential of solar energy utilization in cold climate zones, such as Ulaanbaatar (Mongolia), which is one of the five cities with the worst air quality in the world. Since air pollution contaminates solar collectors and decreases their efficiency, this model accounts for dust deposition behavior so that the best cleaning time for the collectors can be estimated. The simulation results show that the best cleaning time falls between the middle of January and the beginning of February. In addition, a collector cleaned once during the heating period is estimated to produce 12% more energy compared with a collector that has not been cleaned.

In this study investigated the effects of dust deposition on transmittance of glass tubes of a solar thermal collector in capital city Ulaanbaatar of Mongolia, during winter season. We will presents the results based on exposure tests conducted between October 2015 and May 2016. The dust deposition on glass tubes was empirically modeled using the test results and the environmental data such as wind speed and direction, daily average airborne dust rate, snow, and rain. Based on observation of long- and short-term tests, snow was deduced to be able to clean dust accumulation on the glass tubes even if the ambient temperature is below zero. Also, the snow was found to be more effective in decreasing dust accumulation than rain according to the estimation.

The present study investigated the effects of dust deposition on transmittance of glass tubes of a solar thermal collector in Ulaanbaatar during the cold period and presents the results based on real exposure tests conducted between October 2015 and May 2016. In addition, the dust deposition on glass tubes was empirically modeled using the test results and the environmental data such as wind speed and direction, daily average airborne dust rate, snow, and rain. Based on observation of long- and short-term tests, snow was deduced to be able to clean dust accumulation on the glass tubes even if the ambient temperature is below zero. Also, the snow was found to be more effective in decreasing dust accumulation than rain according to the estimation.

Ulaanbaatar is one of the top five cities with worst air quality because of the use of raw coal in the Ger District. The objective of this paper is to analyze the environmental significance of solar heating system with triple heat sources including the solar collector, coal stove, and electric heater for a simple detached house in the harsh cold and dry climate. A long-term measurement was conducted in a Ger district of Ulaanbaatar city, Mongolia. Based on the measurement of the solar thermal system for 8 months, from October 2015 to May 2016, it was found that the average collector efficiency was 49.8% and system efficiency was 37%. The study also reported monthly performance of the system. The dust deposition on the glass tubes of the evacuated tube solar collector considerably decreased the performance, especially during winter season.