Annual Simulation of Phase Change Materials for Enhanced Energy Efficiency and Thermal Performance of Buildings in Southern California

Abstract

The use of advanced thermal storage materials, such as phase change materials (PCMs), offers a practical approach to reducing energy consumption in buildings while maintaining comfortable indoor temperatures. This work employs EnergyPlus to simulate the energy consumption of residential homes equipped with paraffin-based PCMs in Southern California, a region that experiences extremely high summer temperatures and significant day–night temperature variations. Two computational methods, the basic method and the hysteresis method, are employed. The effect of position, melting point, thickness, and thermal conductivity of PCMs on the energy savings rate in buildings is systematically investigated. The results show that the optimized melting point of PCM for Riverside and Palm Springs falls within the range of 19 to 21 °C. As thermal conductivity increases from 0.2 W m⁻¹ K⁻¹ to 3 W m⁻¹ K⁻¹, energy consumption in Riverside decreases by about 5%, whereas in Palm Springs, with its hotter summer temperatures, energy consumption increases. The optimal parameters yielded a total annual energy savings rate of 35.24% in Riverside and 18.52% in Palm Springs using the basic method and 35.47% in Riverside and 22.13% in Palm Springs using the hysteresis method. Under natural ventilation conditions, PCMs can reduce indoor day–night temperature differences in summer to 2.4 °C and 2.2 °C in Riverside, depending on the method used, compared to a 7 °C temperature difference without PCMs. Even without air conditioning, PCMs effectively maintain indoor temperatures within a comfortable range. This work demonstrates that optimizing PCMs in building design can significantly enhance energy efficiency and thermal comfort, providing a sustainable solution for reducing energy demands in residential settings.

1. Introduction

As cities continue to develop, the demand for energy rises each year. From 1960 to 2022, energy consumption in the United States increased by 470% and is still growing at a rate of 3% per year [1]. The energy consumption of residential and commercial buildings accounts for a high proportion of the total energy consumption, and this proportion is also increasing year by year. In 2012, housing energy consumption accounted for 29% of total consumption in California, and by 2022, the proportion of housing energy consumption had increased to 35% [2]. Consequently, finding new and innovative ways to save energy and lower residents’ electricity costs has become a prominent topic in the fields of construction and energy storage. For example, in Southern California, temperature regulation accounts for 24% of total electricity use in residential homes, and households in California spend over USD 1000 per year on heating and cooling [1]. If a house can naturally regulate its indoor temperature within a comfortable range through its structure or constituent materials, it can significantly reduce overall energy consumption.

Thermal energy storage systems provide a modern solution for energy management by enhancing energy efficiency, promoting sustainability from a building materials perspective. These thermal energy storage systems can be categorized into three main types. (1) Sensible heat storage [3] leverages a material’s specific heat capacity to raise the temperature of a storage medium without changing its phase, such as water, molten salts, or rocks. Sensible heat storage systems tend to be cost-effective and are easy to implement but require large volumes due to their low energy density. (2) Thermochemical energy storage systems [4] store energy through reversible chemical reactions and offer a high energy density with long-term stability but require complex infrastructure and high initial investment. (3) Latent heat storage systems utilize phase change materials (PCMs) to store energy during phase transitions, such as the one from solid to liquid. Compared to the other thermal energy storage systems, latent heat storage boasts a high energy density, a moderate initial investment, requires little space, is generally lightweight, and makes use of the large temperature variations between day and night [5].

PCMs are a viable and effective option for reducing HVAC expenses—referring to the costs of heating, ventilation, and air conditioning—associated with residential homes. PCMs have the ability to store thermal energy through the latent heat of fusion during a phase transition. As a result, they can buffer thermal fluctuations within buildings and store solar thermal energy for short-term or seasonal use [6]. Organic PCMs such as paraffin are particularly suited for thermal energy storage systems in residential applications as they are affordable, chemically inert, chemically stable, non-corrosive, non-toxic, thermally stable, exhibit little to no sub-cooling, possess a tunable melting point based on chemical composition, and have a high natural latent heat of fusion. Paraffin is also a petroleum byproduct, making its use as a thermal energy storage system environmentally conscious [5]. PCMs have widespread applications in the food industry [7,8], transportation and storage [9,10], electronic thermal management [11], and thermal energy storage in buildings [12]. Their integration into construction materials significantly enhances thermal storage capacity, reducing energy consumption and greenhouse gas emissions. For instance, adding 20 mm thick PCM panels to walls and roofs can notably decrease electricity usage by improving thermal regulation [12].

PCMs provide higher energy storage density and maintain nearly constant temperatures during phase transitions, outperforming passive technologies like insulation in dynamic environments. Unlike active systems such as HVAC, PCMs require no external energy input, making them more sustainable and cost-effective over time. Additionally, PCMs complement both passive and active technologies by reducing peak thermal loads and stabilizing indoor climates.

There has been extensive research on the application of PCMs in building structures. Alexander et al. [13] simulated the energy-saving potential of adding microencapsulated PCMs to residential exterior concrete in California climate zones. For houses with a volume fraction of PCMs in concrete ranging from 10% to 30%, they found that the annual cooling load in San Francisco and Los Angeles decreased by 85% to 100% and 53% to 82%, respectively. They also found that cost savings reached their maximum when the phase transition temperature approached the setting temperature. However, microcapsule production is complex and the return on investment is relatively low compared to the annual cost savings, which range from USD 36 to USD 42 in San Francisco and USD 94 to USD 143 in Los Angeles.

Ascione et al. [14] conducted a series of experiments to reduce the cooling demand of buildings under Mediterranean climate conditions. They utilized the EnergyPlus simulation package and employed a one-dimensional conduction finite difference solution algorithm to evaluate the energy savings performance of the buildings. They studied the energy efficiency of different Mediterranean regions such as Ankara, Athens, and Naples, and also investigated the comfort of houses by testing their non-overheating time. It was found that the optimal melting temperature in Ankara was 29 °C, resulting in a 7.2% reduction in cooling energy consumption and a moderate improvement in thermal comfort time.

Mohammad et al. [15] used EnergyPlus to simulate an office building located in Arak, Iran. The study used two types of PCMs, Bio PCM from Phase Change Solutions and Rubitherm’s RT21HC, both with a melting point of 21 °C. The impact of PCM placement within the walls on energy efficiency was considered, and the energy savings rates were analyzed for two specific periods of the year, one half allocated for heating, and the other for cooling. They found that different PCMs have different energy-saving effects depending on their placement within the wall. Bio PCM can reduce the cooling load on the outer layer but is ineffective in the winter. Conversely, when placed in the inner layer, it increases the cooling load but significantly reduces the heating energy consumption. For RT21HC, placing it in the outer layer can cause a sharp increase in cooling load during the three months of the cooling period, but placing it in the inner layer can reduce heating energy consumption by 80%. Additionally, it was found that continuously increasing the thickness of the PCM layer does not contribute to energy conservation.

Table 1. Summary of previous studies on the use of PCMs in buildings to reduce energy consumption.

Performance	Location	PCM	Melting Point (°C)	Latent Heat (kJ kg−1)	Reference
The maximum energy reduction is 32%	Singapore	N/A *	28	223	[16]
The maximum energy reduction is 12.9%	China	RT-27	28	179	[17]
The maximum energy reduction is 15%	Spain	RT-27 SP-25	28; 26	179; 180	[18]
The gypsum plaster with 45% PCMs can store 5 times more energy per unit mass than a thermal brick	Periodic variations of the temperature	Micronal DS 5001X	26	110	[19]
The maximum temperature in the wall with PCM has a time delay around 2 h	Spain	Micronal PCM (BASF)	26	110	[20]
With awnings, temperature peaks are lowered by 3–4 °C (6%)	Spain	Micronal PCM (BASF)	26	110	[21]
The temperature of the room was decreased 3 °C	USA (California)	N/A	25	N/A	[22]
The energy reduction is up to 28.6%	Cyprus	M91 (BioPCM™)	29	165–200	[23]
The peak-hour heat gains are reduced by 23–27% for Marseille and 21–25% for Cairo	Warsaw, Poland Marseille, France Cairo, Egypt	Bio-based PCM: properties determined by HFMA	23	115	[24]
The energy consumption of the warehouse is reduced by 26.05%	Lhasa	N/A	8	221	[25]
The energy requirement decreases around 31%	New Zealand	RT-20	18–22	150	[26]

* “N/A” is an abbreviation for “not applicable”.

summarizes previous work [16,17,18,19,20,21,22,23,24,25,26] on the application of PCMs in buildings to reduce energy consumption. Figure 1 shows that when the thickness of PCMs is approximately 20 mm and the melting point is between 20 °C and 30 °C, the reported energy savings are concentrated between 15% and 35% [14,16,17,18,23,26,27,28,29]. Despite previous studies, several challenges and limitations remain. Many investigations have not compared the differences in energy savings of PCMs during cooling and heating seasons, and there is limited research on whether the same PCM conditions can maintain a comfortable room temperature in both summer and winter. Additionally, there are limited studies on the mutual influence between different properties of PCMs, such as melting point, thickness, and thermal conductivity. Typical research locations are primarily in Asia and Europe, and there is relatively little research on the application of PCMs in buildings in North America.

This study aims to simulate the impact of various PCM parameters on energy consumption and identify the optimal combination for maximizing energy efficiency. We employed two distinct methods to model the role of PCMs in residential buildings, focusing on Southern California. Riverside and Palm Springs were chosen for simulation due to their extremely high summer temperatures and substantial day–night temperature variations. These locations were chosen specifically for their pronounced temperature fluctuations, a factor rarely explored in previous studies. The effectiveness of PCMs depends on the magnitude of temperature fluctuations between day and night. Larger variations ensure complete phase transitions, maximizing energy storage efficiency. If nighttime temperatures remain too high, incomplete solidification can reduce performance. Conversely, colder nights can increase heating demand, which PCMs mitigate by gradually releasing stored heat. We utilized EnergyPlus to simulate the energy consumption of residential homes equipped with PCMs.

This paper is organized as follows: first, we introduce the materials and methods utilized in this research. Next, we analyze the impact of the PCM layer’s position, thickness, melting point, and thermal conductivity on energy savings in Riverside and Palm Springs. Specifically, we describe and compare two simulation approaches: the basic method and the hysteresis method. Additionally, we discuss the regulation of indoor temperature in each region when using PCM.

2. Materials and Methods

2.1. Building Model

The architectural model used in this study is shown in Figure 2, which was modeled using SketchUp and imported into OpenStudio for energy simulation. OpenStudio 3.7.0 is an open-source energy analysis software, and its SDK tool, OpenStudio SketchUp Plug-in, integrates OpenStudio functionality into SketchUp models. Subsequently, importing the model into OpenStudio can assist building designers in analyzing the energy consumption within the building [30].

This building model simulates a residential home located in Southern California, as shown in Figure 2a. The difference between the interior and exterior layers is illustrated in Figure 2c,d, where the PCM layer is placed either inside or outside the insulation material. The simulated house is a single-room residential structure measuring 10 m in width and 15 m in length, with a total area of 150 m². The walls have a height of 3 m, while the roof height is 2 m. The total internal volume of the building is 586.76 m³. The house features glass windows on all sides, with a door measuring 2 m in height and 1 m in width located on one of the exterior walls.

2.2. Material Properties

OpenStudio’s building energy consumption simulations are built upon the EnergyPlus engine [30]. EnergyPlus is a comprehensive building energy simulation program used by engineers, architects, and researchers to model energy consumption, including heating, cooling, ventilation, lighting, plug loads, and process loads, as well as water usage in buildings. It also supports the integration of PCMs into the building material properties. EnergyPlus is also widely used in the field of dynamic energy simulation research [31,32,33,34].

EnergyPlus 24.1.0 was chosen for this study due to its widespread use in building energy simulations, comprehensive modeling capabilities, whole-building simulation, integrated heat balance approach, and flexibility in HVAC modeling. Additionally, it is open-source and backed by the U.S. Department of Energy (DOE), which gives readers an easy way to reproduce or expand upon this study [35]. EnergyPlus also allows for the integration of customizable building materials and specific weather data for our desired locations and supports integration with other tools and plug-ins such as OpenStudio.

The properties of common building materials used in walls are listed in

Table 2. The properties of common building materials [35].

Material	Thickness (m)	Density (kg m−3)	Specific Heat (J kg−1 K−1)	Thermal Conductivity (W m−1 K−1)
Concrete	0.2	2243	837	1.73
Wall Insulation	0.05	91	840	0.04
Roof Insulation	0.21	265	836	0.05
Roof Membrane	0.0095	1121	1460	0.16
Gypsum (Plaster Board)	0.012	785	830	0.16

. Thermal properties, such as thermal conductivity and specific heat, play a crucial role in simulating heat exchange, and EnergyPlus is highly sensitive to these data. In addition to thickness and density, material properties such as thermal absorption and solar absorption can also be inputted into EnergyPlus, enabling more accurate predictions of building energy consumption.

2.3. PCM Modeling Using EnergyPlus

Many previous studies used EnergyPlus for building energy simulations and verified its accuracy [36,37,38,39]. With increasing research using EnergyPlus simulations, this software has undergone continuous improvement. Tabares Velasco et al. [40] validated the PCM function in EnergyPlus and fixed two bugs in the original PCM model using ASHRAS Standard 140, accelerating runtime and allowing simulation of PCMs with variable thermal conductivity.

In our simulation, several parameter settings in EnergyPlus need to be adjusted, including material layer thickness, material density, specific heat, and thermal conductivity. For the hysteresis method, more precise thermal properties of PCM are required, such as peak melting and solidification points, temperature ranges for melting and solidification, densities in solid and liquid states, specific heat, thermal conductivity, and, most importantly, the latent heat of PCM. The simulation runs with a time step of six intervals per hour and covers a duration of one year.

2.3.1. The Finite Difference Solution Algorithm

According to the EnergyPlus documentation [35], the one-dimensional conduction finite difference (CondFD) algorithm can be used to simulate materials such as PCMs that undergo significant and frequent property changes. The CondFD algorithm, shown below, adopts the implicit finite difference scheme.

$$C_p\rho ∆x{\displaystyle \frac{T_i^{j+1}-T_i^j}{∆t}}={\lambda }_{\omega }{\displaystyle \frac{T_{i+1}^{j+1}-T_i^{j+1}}{∆x}}+{\lambda }_E{\displaystyle \frac{T_{i-1}^{j+1}-T_i^{j+1}}{∆x}}$$

(1)

Here, T is temperature, i are the modeling nodes, i + 1 represents adjacent nodes inside the building structure, i − 1 represents adjacent nodes in the outer direction, and j + 1 represents the current simulation time step. j represents the previous time step, $∆t$ represents the time step, $∆x$ is the thickness of the finite difference layer, $C_p$ represents the specific heat, ρ represents the density of the material, and ${\lambda }_{\omega }$ and ${\lambda }_E$ represent the thermal conductivity of the interface between node i and i + 1 and between node i and i − 1.

$$C_p={\displaystyle \frac{h_i^j-h_i^{j-1}}{T_i^j-T_i^{j-1}}}.$$

(2)

Due to the variable specific heat of PCM, EnergyPlus uses the algorithm in (2) to update and iterate the values of $C_p$, where h represents specific enthalpy.

2.3.2. Basic Method and Hysteresis Method

EnergyPlus provides two methods for the CondFD algorithm to process the temperature-dependent enthalpy curve of materials, which is also one of the methods for simulating how software processes PCMs:

• The Basic Method: this method uses the single thermal enthalpy curve for melting and solidification, requiring at least four sets of temperature/enthalpy data points to be input as shown below. The input data are displayed on the temperature enthalpy curve [14] as shown in Figure 3.
• The Hysteresis Method: this method uses non-isothermal enthalpy curves during melting and solidification, requiring more accurate PCM thermal properties such as thermal conductivity, specific heat capacity, and densities of both solid and liquid phases. It also requires input of the peak melting temperature and temperature ranges for melting and solidification [41].

We use these two methods for simulation experiments for comparison. For the basic method, we use a simulated PCM temperature-dependent enthalpy curve with a latent heat of 200 kJ kg⁻¹ and a phase transition temperature range of 2 °C. For the hysteresis method, we use the thermal properties of PCMs provided by Rubitherm [40] for simulation, as listed in

Table 3. Thermal properties of Rubitherm RT PCMs [42].

Name *	Peak ${\mathit{T}}_{\mathit{m}}/{\mathit{T}}_{\mathit{s}}$ (°C)	${\mathit{H}}_{\mathit{f}}$ ($\mathbf{k}\mathbf{J} {\mathbf{k}\mathbf{g}}^{-1}$)	${\mathit{C}}_{\mathit{p}\mathit{l}}/{\mathit{C}}_{\mathit{p}\mathit{s}}$ ($\mathbf{J} {\mathbf{k}\mathbf{g}}^{-1} {\mathbf{K}}^{-1}$)	${\mathit{\rho }}_{\mathit{s}}/{\mathit{\rho }}_{\mathit{l}}$ ($\mathbf{k}\mathbf{g} {\mathbf{m}}^{-3}$)	${\mathit{\lambda }}_{\mathit{s}}/{\mathit{\lambda }}_{\mathit{l}}$ ($\mathbf{W} {\mathbf{m}}^{-1} {\mathbf{K}}^{-1}$)
RT18HC	18/17	260	2	880/770	0.2
RT21HC	21/21	190	2	880/700	0.2
RT22HC	22/22	190	2	760/700	0.2
RT24HC	24/24	200	2	800/700	0.2
RT25HC	25/25	230	2	880/770	0.2
RT28HC	28/27	250	2	880/770	0.2

* $T_m/T_s$ are peak temperature of melting and solidification. $H_f$ is heat of fusion. $C_{ps}/C_{pl}$ are specific heat of solid and liquid. ${\rho }_s/{\rho }_l$ are densities of solid and liquid. ${\lambda }_s/{\lambda }_l$ are thermal conductivities of solid and liquid.

.

3. Results and Discussion

Various PCM properties were adjusted to identify the most effective energy-saving solution, which was then analyzed for its performance in maintaining indoor temperatures under natural ventilation. Natural ventilation refers to the circulation of air within a building without the use of mechanical systems, relying instead on natural forces like wind and thermal buoyancy to move air through openings such as windows and doors. The energy consumption and savings rates reported in the simulation results correspond to both cooling and heating energy usage and savings.

3.1. Annual Temperature of Testing Locations

Riverside and Palm Springs were selected as testing locations due to their unique climates and PCM applicability. Riverside has a semi-arid climate, with dry, hot summers and mild, relatively wet winters. Palm Springs has a desert climate, which is dry and has large day to night temperature variations [43]. The temperature of the two cities throughout the year is shown in Figure 4. Riverside’s lower winter temperatures offer a suitable environment to assess the performance of PCMs, which are typically optimized for reducing heat exchange during warmer conditions. Conversely, the significant day-to-night temperature variation in Palm Springs provides an ideal setting to examine the dynamic behavior of PCMs over a single day.

3.2. Basic Method Results

3.2.1. Position of PCMs

The position of the PCM layer within the exterior wall was tested first. We evaluated the energy-saving performance of the PCM layer placed both inside and outside the insulation material, as shown in Figure 2. It should be noted that most houses in Southern California incorporate insulation materials to maintain indoor temperature. Our model examines the effects of placing the PCM layer either on the interior or exterior side of the insulation material.

Compared to the total annual cooling and heating energy consumption without the use of PCM, shown in Figure 5a, placing PCM with a melting point of 19 °C in the outer layer can achieve up to 26.85% energy savings, while placing it in the inner layer can save 35.25%. We used different melting points to verify the accuracy of the results. As shown in Figure 5b, placing PCM in the inner layer, as opposed to the outer layer, reduces annual cooling and heating energy consumption by an average of 166.67 kWh. This result is similar to that of Mohammad et al. [15], which suggests that placing the PCM layer inside the wall is more conducive to energy conservation. Therefore, in all subsequent simulations, we place the PCM layer in the inner layer to optimize energy efficiency.

3.2.2. Thickness for Basic Method

We conducted energy simulations on walls with varying PCM layer thicknesses ranging from 5 mm to 100 mm using PCM temperature-dependent enthalpy curves with a melting point of 19 °C. The results are shown in Figure 6. When the thickness of the PCM layer is less than 30 mm, there is a significant improvement in the heating and cooling energy savings rate. Once the thickness exceeds 30 mm, the energy savings rate ceases to show significant improvement and may even decline, indicating that further increases in thickness do not enhance the energy-saving effect. As a result, the PCM layer has an optimal thickness of about 30 mm.

We also compared the thickness-dependent energy savings rates for Riverside and Palm Springs in Figure 6. The results indicate that PCMs achieve greater energy savings in Riverside. This is partly due to the high temperatures in Palm Springs during summer, which can reach up to 41 °C (106 °F), resulting in the heat exchange between the house and the outdoors exceeding the latent heat storage capacity of the PCM layer. In this case, PCM melts prematurely, making it unable to function properly during the subsequent daytime.

3.2.3. Melting Point for Basic Method

The melting point of a PCM is a crucial attribute in the latent heat energy storage process, determining whether the PCM can absorb heat in hot weather and release heat to maintain room temperature equilibrium after the temperature drops. We tested the energy-saving effect of various PCMs with a melting point ranging from 17 °C to 28 °C. In the basic method simulation, the two cities exhibited different melting point energy consumption histograms as shown in Figure 7.

In Palm Springs, energy consumption is more sensitive to the melting point. The lowest energy consumption occurs at 20 °C and 26 °C, which are close to the set indoor temperatures for simulated cooling and heating. A study by Alexander et al. [13] suggested that the optimal melting point is achieved when its melting temperature is close to the indoor set temperature of 20 °C, maximizing energy-saving performance.

In Riverside, the energy consumption is less sensitive to melting point compared to Palm Springs, and the energy consumption using PCMs with a melting point of 26 °C in Riverside does not show the same high reduction as in Palm Springs. The optimal melting point temperature has decreased, with Riverside showing an optimal melting point of 19 °C, compared to 20 °C in Palm Springs. This may be related to the average temperature of the two cities. Palm Springs requires a slightly higher melting point due to its higher outdoor temperature to ensure longer heat absorption time in hot weather. However, despite the difference in melting points, the optimal melting point for energy-saving consumption is still close to the indoor set temperature, which is consistent with the previous research [13].

3.2.4. Thermal Conductivity for Basic Method

The low thermal conductivity of PCMs has always been an obstacle to their widespread implementation. The thermal conductivity of pure paraffin is approximately 0.2 W m⁻¹ K⁻¹ [13]. The use of thermally conductive fillers or porous metal foams can greatly enhance the thermal conductivity without significant changes to the latent heat or thermal energy storage capabilities [44,45,46]. For example, incorporating expanded graphite or graphene nanoplatelets can enhance the thermal conductivity of paraffin to over 2 W m⁻¹ K⁻¹ at filler fractions below 10 wt% [47]. Therefore, we have simulated the energy-saving effects of PCMs with different thermal conductivities as shown in Figure 8.

Based on our simulation, we found that the impact of thermal conductivity of PCMs on buildings does not achieve the significant energy consumption reduction caused by thickness and melting point. In Riverside, the highest energy savings rate reaches roughly 4%. The HVAC energy consumption reduces from 6.25 GJ to 6.03 GJ, when the thermal conductivity increases from 0.2 W m⁻¹ K⁻¹ to 3 W m⁻¹ K⁻¹. The energy savings rate does not further increase when the thermal conductivity is beyond the threshold of 2–3 W m⁻¹ K⁻¹, indicating that 2–3 W m⁻¹ K⁻¹ is the optimal thermal conductivity in Riverside. However, in Palm Springs, the energy consumption data show a different behavior. Increasing the thermal conductivity beyond 0.3 W m⁻¹ K⁻¹ does not reduce energy consumption; instead, it leads to an increase.

To better understand this result, we simulated the HVAC energy consumption for a single month in two locations, as shown in Figure 9. The results indicate that increasing thermal conductivity reduces energy consumption during the warm and cold seasons but leads to higher energy consumption during the hot summer months (June to September). This trend is observed in both Riverside and Palm Springs simulations. The increase in thermal conductivity enhances the heat exchange rate of the house in summer, leading to higher energy consumption during the summer season. In Palm Springs, where summer temperatures are extremely high, this effect is more pronounced compared to Riverside. Additionally, Palm Springs has shorter warm and cold seasons than Riverside, resulting in an overall increase in energy consumption.

3.2.5. Basic Method Summary

The energy savings rates for various thicknesses and melting points under Riverside weather conditions are shown in Figure 10. The highest energy savings rate of 35.24% can be achieved with a thickness of 30 mm and a melting point of 19 °C. The result is comparable to the reported maximum energy savings rate of 33% in Iran in a previous study [27].

3.3. Hysteresis Method Results

Next, we discuss the results based on the hysteresis method. We used the thermal properties specified in Rubitherm’s product data. However, these properties do not cover the full melting temperature range of 18 to 28 °C, as shown in the Section 3.2. Instead, we selected the closest parameter settings available from Rubitherm and verified that these settings are the most energy-efficient.

3.3.1. Thickness for Hysteresis Method

The results shown in Figure 11 indicate that RT21HC in Riverside has a higher energy savings rate, and energy savings are maximized when the thickness is close to 50 mm. This optimized thickness is larger than the simulation results predicted by the basic method. In Palm Springs, the energy savings rate continues to increase beyond 50 mm, although the growth rate slows after this point. Due to the high temperatures in Palm Springs, thicker walls absorb more heat, leading to different results compared to the basic method.

Due to the idealized data of the basic method, there may be discrepancies when using more realistic and accurate data. However, considering the construction cost of the house and the low gain between 30 mm and 50 mm, 30 mm is still a relatively good choice.

3.3.2. Melting Point for Hysteresis Method

For the melting point analysis, six high-latent-heat PCMs produced by Rubitherm, with melting points ranging from 18 °C to 28 °C, were selected. The annual cooling and heating energy consumptions in Palm Springs for these PCMs are presented in Figure 12a. It is observed that a lower melting point also improves energy savings, similar to the results obtained with the basic method. However, at excessively low melting points, such as 18 °C, heating energy increased due to the high temperatures preventing the PCMs from completing a full melting–solidification cycle, resulting in higher heating energy consumption.

The annual cooling and heating energy consumptions in Riverside for these PCMs are presented in Figure 12b. It is observed that lower melting points reduce heating energy consumption, as PCMs with lower melting points are more effective at maintaining indoor temperatures during winter. However, an excessively low melting point can cause the PCM to become ineffective during summer, resulting in increased cooling energy consumption. Therefore, based on these findings, RT21HC with a peak melting point of 21 °C achieves the highest energy savings rate.

3.3.3. Thermal Conductivity for Hysteresis Method

The results of varying thermal conductivity using the hysteresis method in Riverside closely align with those obtained using the basic method, as shown in Figure 13. Thermal conductivities of RT21HC and RT25HC were tested, demonstrating that improving thermal conductivity from 0.2 W m⁻¹ K⁻¹ to 3 W m⁻¹ K⁻¹ can effectively reduce energy consumption from 6.96 GJ to 6.63 GJ with RT25HC, which is approximately a 5% energy saving. However, the benefits plateaued near 2–3 W m⁻¹ K⁻¹, with RT21HC even reaching 3 W m⁻¹ K⁻¹, resulting in a slight increase in energy consumption. Consequently, selecting a PCM with a thermal conductivity in the range of 2–3 W m⁻¹ K⁻¹ remains an optimal choice.

In Palm Springs, the energy consumption shows an increasing trend when thermal conductivity exceeds 0.3 W m⁻¹ K⁻¹. This result is consistent with the basic method, indicating that higher thermal conductivity in extremely high-temperature climates increases the energy burden.

3.3.4. Hysteresis Method Summary

We conducted comprehensive simulations of various PCM thicknesses and melting points using the hysteresis method, as shown in Figure 14. The results indicate that using RT21HC with a thickness of 50 mm achieves the highest energy savings rate of 35.47% in Riverside. However, as highlighted in Abdullah’s research [12], excessively thick PCM layers can lead to significantly higher construction costs. Furthermore, the improvement in energy savings rates when increasing the PCM thickness from 30 mm to 50 mm is relatively small and does not justify the additional cost. Hence, a 30 mm PCM layer is recommended as the optimal choice.

3.4. Daily In/Outside Naturally Ventilated Temperature

After simulating PCMs with different attributes using the basic method and hysteresis method, we identified the optimal parameters based on our simulation results. We then implemented a PCM layer with these optimized parameters in a building operating under natural ventilation conditions, where air conditioning was not used for temperature regulation, and daily activities continued as usual. The simulations compared the internal temperature of the building with and without the PCM layer, highlighting the impact of PCMs on the building’s heat exchange rate.

3.4.1. Basic Method

Using the properties optimized through the basic method, as shown in Figure 15, we implemented PCMs with melting points of 26 °C and 20 °C in Palm Springs and PCMs with melting points of 26 °C and 19 °C in Riverside. All PCM layers have a thickness of 30 mm and a thermal conductivity of 2 W m⁻¹ K⁻¹. We compared the indoor temperatures of buildings with and without PCM installation during summer. As shown in Figure 15, the results clearly demonstrate that the installation of a PCM layer significantly stabilizes the indoor temperature. During the day, the PCM melts and absorbs external heat, reducing the heat exchange rate between the interior and exterior of the building. At night, when the outdoor temperature drops, the PCM solidifies and releases heat back into the building, maintaining a comfortable indoor temperature.

In Palm Springs, a PCM with a melting point of 20 °C exhibits the best performance, maintaining an indoor temperature difference of roughly 1.6 °C between day and night, and maintaining lower room temperatures during hot daytime periods. In Riverside, there is no significant difference between the effect of a PCM with a melting point of 19 °C or 26 °C on room temperature, as both can maintain a day–night room temperature difference at around 1.8 °C.

3.4.2. Hysteresis Method

For the hysteresis method results, shown in Figure 16, we used RT21HC and RT25HC with melting points of 21 °C and 25 °C, respectively, to simulate the indoor temperature of naturally ventilated buildings located in Riverside. PCM layers with a thickness of 30 mm and a thermal conductivity of 2 W m⁻¹ K⁻¹ were also compared with buildings without PCM installation. The impact of PCM installation on room temperature in each season is analyzed.

The results show that RT21HC demonstrates excellent room temperature maintenance capabilities. In warmer seasons such as spring and summer, RT21HC is more effective at reducing indoor temperatures compared to RT25HC. The average indoor temperature is maintained between 19.90 °C and 21.62 °C in spring and between 21.20 °C and 23.63 °C in summer. During colder seasons like autumn and winter, RT21HC leverages its low melting point to maintain room temperature effectively throughout the cold season. RT21HC effectively maintains indoor warmth during autumn and winter, with winter indoor temperatures ranging between 19.37 °C and 21.18 °C. In summary, a low-melting-point PCM demonstrates strong performance in maintaining room temperature year-round in Southern California. It should be noted that these results are based on simulations where the PCM layer is placed within the insulation layer. Therefore, maintaining the room temperature within such a stable range is, to some extent, the result of the combined effect of the PCM and insulation materials.

3.5. Comparison Between Basic Method and Hysteresis Method

The simulation results from the two methods demonstrate that the most optimal PCM parameters are largely similar, as listed in

Table 4. Optimal PCM parameters for Riverside and Palm Springs.

Parameters	Riverside (Basic Method)	Palm Springs (Basic Method)	Riverside (Hysteresis Method)	Palm Springs (Hysteresis Method)
Position	Interior	Interior	Interior	Interior
Thickness (mm)	30	30	30–50	>50
Melting Point (°C)	19	20	21	21
Thermal Conductivity ($\mathrm{W} {\mathrm{m}}^{-1} {\mathrm{K}}^{-1}$)	2–3	0.2	2-3	0.2

. Each parameter was systematically tested across a range of values and cross-checked for a range of values of other parameters in order to find the highest energy savings rate. The hysteresis method has a slightly higher energy savings rate than the basic method, with an annual cooling and heating energy savings rate of 35.24% in Riverside and 18.52% in Palm Springs using the basic method and 35.47% in Riverside and 22.13% in Palm Springs using the hysteresis method, respectively.

The simulation results show that the melting point has the greatest impact on energy efficiency, with a contribution near 15%, compared to 10% for thickness and 5% for thermal conductivity.

In terms of PCM layer thickness, the basic method does not show a significant improvement in energy efficiency beyond 30 mm. However, under the hysteresis method, energy efficiency continues to improve beyond 30 mm, with the increase being more pronounced in simulations for Palm Springs. Regarding the melting point, both methods show the lowest energy consumption around 20 °C. For the basic method, the optimal PCM melting point is 19 °C, while in Palm Springs, it is 20 °C. For the hysteresis method, the optimal PCM melting point is 21 °C for both Riverside and Palm Springs. Lastly, regarding thermal conductivity, both methods indicate that, in Riverside, the lowest energy consumption occurs when thermal conductivity is around 2–3 W m⁻¹ K⁻¹. However, in Palm Springs, due to extreme high temperatures, higher thermal conductivity leads to increased energy consumption.

4. Conclusions

This study reports the effect of PCMs on energy-saving and thermal performance of buildings in Southern California. We used the EnergyPlus-based OpenStudio building energy simulation software to compare and analyze two different PCM simulation schemes, the basic method and the hysteresis method, by calculating the heat transfer between different materials using the CondFD algorithm. Starting with the positioning of the PCM in the exterior wall, a series of PCM properties—including thickness, melting point, and thermal conductivity—were studied to determine the optimal combination of these properties. Furthermore, the optimal PCM configuration identified was applied to simulate the indoor temperature of a naturally ventilated building. The main conclusions are as follows:

• The energy savings rate is higher when the PCM layer is installed on the interior side of the insulation layer.
• Continuously increasing the thickness of PCM does not improve energy efficiency and it reaches a critical point at a thickness of around 30 mm in Riverside.
• The results indicated that the optimal PCM melting point for the basic method is 19 °C in Riverside and 20 °C in Palm Springs. For the hysteresis method, the optimal PCM melting point is 21 °C for both Riverside and Palm Springs.
• Using a PCM with a melting point close to the indoor cooling and heating set temperature can lead to a higher energy savings rate, though this effect depends on the regional climate. Cities with lower temperatures and greater heating needs require PCMs with lower melting points.
• Increasing the thermal conductivity of PCMs can effectively reduce building energy consumption in Riverside by approximately 5%. However, further improvements in energy efficiency diminish beyond a certain threshold. In this study, the optimal thermal conductivity is determined to be around 2–3 W m⁻¹ K⁻¹.
• In extremely hot regions, such as Palm Springs, the energy savings rate of PCMs is significantly reduced. Conversely, in cooler areas, such as Riverside, using low-melting-point PCMs can more effectively regulate indoor temperatures during the heating season.
• The optimal parameters yielded a total annual energy savings rate of 35.24% in Riverside and 18.52% in Palm Springs using the basic method and 35.47% in Riverside and 22.13% in Palm Springs using the hysteresis method.

For future studies, this simulation requires further refinement. The single-room layout model should be upgraded to better represent the full house model. Additionally, the impact of additional equipment on indoor temperature and energy consumption must be considered. To validate the simulation results, pilot-scale experiments will be conducted.