Achieving Nearly Zero-Energy Buildings through Renewable Energy Production-Storage Optimization

Abstract

This research focuses on optimizing renewable energy systems to achieve Nearly Zero-Energy Building (nZEB) Level 1 status, defined as reducing energy consumption by 87.5% to 100%. The major objectives are to explore the impact factors in the optimization of energy storage systems (ESSs), solar PV and ESS capacities, as well as energy consumption patterns. This study is based on monitoring data from an office building in Thailand with a 120 kW peak load and a 40 kW average load, equipped with a 160 kW photovoltaic (PV) system and 45 kWh from ESS. This study is based on comparing a simulation of a renewable energy system, particularly from unutilized solar energy, with building load demand to optimize the best system suitability for achieving nZEB Level 1 status. The results indicate that a 200 kW PV system combined with a 275 kWh ESS and a 250 kW PV system with an ESS capacity of 175 kWh can adequately supply the required clean energy demand. These findings provide insights on optimizing factors of renewable energy systems for buildings aiming to achieve sustainability targets. This work has summarized a framework including optimization impact factors with financial aspects which can be applied to similar cases. In addition, an analysis of working-day load profiles and appliance usage patterns has been performed to provide broader consumption insights. This approach identifies trends in HVAC, lighting, and electronics consumption, enabling the optimization scheme to be adapted to buildings with varying load patterns. Additionally, this study examines the effects of building operation hours on energy consumption. By adjusting operational schedules based on these insights, different renewable energy system capacities can be re-estimated to ensure achievement of the desired nZEB Level.

1. Introduction

The global push towards sustainable development has placed Zero-Energy Buildings (ZEBs) [1] at the forefront of efforts to reduce energy consumption in the built environment. ZEBs [2,3] are designed to produce as much energy as they consume over a given period, typically on an annual basis, primarily through on-site renewable energy sources. Achieving such a balance requires the integration of advanced energy systems, including photovoltaic (PV) solar panels, energy storage solutions, and intelligent energy management systems [4] that collectively contribute to energy self-sufficiency [5].

1.1. Background and Significance

Energy consumption in buildings accounts for nearly 40% of global energy demand [6], with commercial and residential sectors being major contributors. This significant energy consumption highlights the need for energy-efficient buildings that minimize reliance on non-renewable sources [7]. Zero-Energy Buildings (ZEBs) have emerged as a key solution to reduce energy consumption, enhance energy security [8], and mitigate the impact of volatile energy prices. As part of the broader Net Zero goal [9], reducing greenhouse gas emissions from buildings is critical in achieving sustainability across sectors.

The 2050 Net Zero goal [10] necessitates a significant reduction in greenhouse gas emissions across all sectors, and buildings, as one of the largest energy consumers, play a vital role in this transition. The building in this study is a 4600-square-meter office facility with 2300 square meters of air-conditioned space, located in northern Thailand. Benefiting from high solar potential, with 17–19 MJ/m² of solar radiation per day, it is equipped with a 160 kW solar photovoltaic (PV) system and a 50 kW energy storage system (ESS). These features make it an ideal case for exploring renewable energy optimization toward achieving Nearly Zero-Energy Building (nZEB) status. The building’s energy profile, dominated by air-conditioning, lighting, and office equipment, reflects typical commercial office buildings in tropical climates. With its renewable infrastructure and detailed energy usage data, this building offers a practical platform for investigating dynamic energy management strategies that integrate solar generation, storage, and changing operational behaviors.

While Zero-Energy Buildings (ZEBs) have been widely studied, much of the existing literature focuses on either technical optimization of renewable systems or static energy consumption models. However, fewer studies have explored the integration of behavioral analysis with energy system optimization to adjust for fluctuating operational schedules and occupant behavior. This study fills that gap by focusing on the combined optimization of solar PV and ESS while incorporating real-time behavioral insights to achieve Nearly ZEB Level 1 in a commercial office building located in a tropical climate. This unique approach aims to provide a scalable, adaptable model for similar buildings and regions.

Achieving Nearly ZEB Level 1, where energy consumption is reduced by 87.5% to 100%, is a significant focus of this research. This milestone represents an important step toward the 2050 Net Zero target, as it demonstrates how solar energy and ESS can be used to minimize a building’s energy footprint. This research aims to optimize the interaction between solar energy generation and storage to provide scalable solutions for energy efficient buildings with reduced dependency on external power sources. Numerous studies have explored various aspects of optimization, such as optimizing the structure of buildings for energy use prediction [11], which further supports the approach taken in this research to enhance energy efficiency in Nearly Zero-Energy Buildings (nZEBs).

In addition to the technical optimization of PV and ESS, this study incorporates behavioral analysis to enhance energy efficiency. A building’s energy consumption is dynamic, influenced by operational schedules, occupancy, and other variables. By analyzing these behavioral patterns, this study seeks to optimize the energy system in real time, adjusting solar generation and storage to match the changing needs of the building throughout the day. This approach allows for a more adaptable energy management system, particularly in buildings with varying operational schedules, such as offices with seasonal fluctuations or irregular working hours.

The integration of behavioral analysis with technical optimization offers a flexible approach to energy management. By simulating different operational scenarios, this research identifies how solar generation and storage can be adjusted to minimize energy losses and improve efficiency. This dynamic approach ensures that the building operates efficiently under changing conditions.

By combining solar energy system and ESS optimization with behavioral insights, this research provides valuable contributions to the ZEB field. It offers practical guidance for building managers, engineers, and policymakers, emphasizing the need for energy systems that adapt to both static and dynamic building conditions. The findings are particularly relevant to regions with similar climatic conditions and solar energy potential, where the strategic use of PV systems, ESSs, and behavioral insights can significantly reduce the environmental impact of commercial buildings.

1.2. Research Objectives

The primary objective of this research is to optimize the performance of the building as a ZEB by integrating advanced solar energy systems with battery storage and behavioral analysis for predictive analysis. This study seeks to explore the following specific goals:

• Optimization of Energy Storage Systems (ESSs): This research aims to optimize the energy storage system (ESSs) capacity within the building to enhance overall energy efficiency and minimize energy losses. This optimization process is closely aligned with the energy performance classification system developed by the Guideline for ZEB definition and evaluation by SHASE Japan [12], which categorizes buildings into four distinct zones based on energy usage reduction: ZEB Oriented Buildings (35–50%), ZEB Ready (50–75%), Nearly ZEB Level 2 (75–87.5%), and Nearly ZEB Level 1 (87.5–100%) As shown in Figure 1. By simulating various ESS capacities ranging from 0 to 500 kWh, this study seeks to identify the optimal capacity that will propel the building towards the highest level of energy performance, Nearly ZEB Level 1, where the building becomes nearly self-sufficient with minimal energy losses.
• Impact of Solar PV System and Energy Storage Capacity: In addition to optimizing the ESS, this study examines the impact of different solar PV system capacities (160, 180, 200, and 250 kW) on the building’s energy management. The SHASE classification system is also applied here to evaluate how varying the PV system size influences the building’s progress through the energy performance zones. The goal is to determine the most effective combination of solar PV capacity and ESS that will maximize energy efficiency and allow the building to achieve Nearly ZEB Level 1 status. By understanding the interaction between solar generation and storage, this research provides insights into how different system configurations can either hinder or advance the building’s journey towards optimal energy performance.
• Behavioral Analysis: This research aggregates energy consumption data to simulate appliance usage patterns based on typical working-day behaviors and appliance percentage ratios. This analysis aims to uncover critical insights into the consumption patterns of HVAC systems [13], lighting [14], and electronic devices [15], thereby enabling targeted energy-saving interventions [16].

1.3. Methodological Approach

The optimization of solar photovoltaic (PV) and energy storage systems (ESSs) in this study is key to advancing the building’s energy performance toward Nearly ZEB Level 1, where energy consumption is reduced from 87.5% to 100%. To determine the optimal system sizes, simulations are conducted by varying solar PV capacities and ESS storage. These simulations are designed to explore how different configurations of energy generation and storage interact with the building’s energy consumption patterns throughout the day. Similar approaches, such as optimizing the siting and sizing of ESSs to address challenges like voltage impact in distribution systems [17], have been shown to improve energy efficiency in renewable energy setups. In this study, optimizing the size of the solar PV and ESS ensures the building generates sufficient energy during peak sunlight hours and stores enough energy to meet demand during non-solar periods, thereby minimizing reliance on external power sources.

The SHASE classification system is applied to evaluate the energy performance across different zones within the building, following the ZEB definition [18]. This framework allows this study to assess how well each zone performs in relation to the overall goal of Nearly ZEB Level 1. The SHASE system enables a granular analysis of the building’s energy consumption, highlighting areas where additional optimization is required. The combination of optimized solar PV and ESS capacities, together with the SHASE classification system, provides a comprehensive framework for improving the building’s energy efficiency. This approach not only identifies key zones for further improvement but also ensures that the building’s energy systems are adjusted to meet actual consumption more effectively.

The integration of behavioral analysis [19] breaks down energy consumption into key contributors such as HVAC systems [20], lighting [21], and electronic devices [22,23]. By simulating usage patterns based on typical working-day behaviors, this research offers a dynamic understanding of the building’s energy consumption. These behavioral patterns are integrated into the simulations, allowing for real-time adjustments to the energy system, optimizing both solar generation and storage throughout the day. The novel aspect of this approach is the continuous adjustment of energy systems based on actual building behavior, resulting in more accurate and efficient energy management.

Simulation results are validated against actual energy consumption data to ensure both accuracy and reliability. This validation process strengthens the applicability of the findings by demonstrating that the system can adapt to real-world conditions.

2. Material and Methods

2.1. Case Study Building

The Energy Research and Development Institute, Chiang Mai University (ERDI-CMU) building, functioning as an office facility of 100 kW size, is equipped with a 160 kW solar photovoltaic (PV) system and a 50 kW–45 kWh energy storage system (ESS) intended to meet its energy demands sustainably as illustrated in Figure 2 and Figure 3.

Despite this setup, significant energy losses have been identified within the current solar generation and storage system, hindering the building’s progression toward Nearly Zero-Energy Building (nZEB) Level 1 status.

This building aims to address these energy inefficiencies through comprehensive analysis and optimization strategies under the Sustainable Green Energy Management System program. The project also incorporates advancements in IoT-enabled transformers and collects data energy management from private applications shown in Figure 4.

2.2. Problem Formulation

The primary objectives of this study are as follows:

• Quantify and Analyze Energy Losses: Identify and evaluate the magnitude and causes of energy losses within the current 160 kW solar PV and 50 kW ESS configuration.
• Optimize Energy Storage Capacity: Determine the optimal ESS capacity between 0 and 500 kWh that minimizes energy losses and maximizes efficiency under existing operational conditions.
• Assess Impact of Varying PV Capacities: Explore how different solar PV capacities (160 kW, 180 kW, 200 kW, and 250 kW) affect overall energy performance and contribute to reducing energy losses.
• Analyze Behavioral Energy Consumption Patterns: Focus on typical office building behaviors and grouped appliance percentage ratios to dissect energy usage patterns. Develop targeted interventions for key systems such as HVAC, lighting, and electronics to further enhance energy efficiency.

By systematically addressing these objectives, this research seeks to develop effective strategies for reducing energy losses, optimizing renewable energy utilization, and advancing the ERDI Chiangmai building towards achieving Nearly ZEB Level 1 status. The findings are expected to contribute valuable insights into sustainable energy management practices applicable to similar office buildings and contexts.

2.3. Data Preparation

To facilitate this analysis, an extensive dataset was collected over a 6-month period from the ERDI Chiangmai building, encompassing comprehensive operational data of the current energy systems as shown in Figure 5.

2.4. Method

The integration of solar energy systems with ESS storage requires careful analysis to achieve optimal performance. The following flowchart (Figure 6) outlines the step-by-step process of simulating and optimizing the solar system’s capacity alongside the energy storage system (ESS). This process is further detailed in Algorithm 1, which demonstrates the simulation and optimization procedure by selecting a range of potential solar sizes (160, 180, 200, 250 kW) and ESS storage capacities (0–500 kWh with increments of 25 kWh) to evaluate the system’s performance across different zones. The comparison of real energy consumption and net energy consumption, as shown in Figure 7, highlights the impact of these optimizations over time.

Algorithm 1: Simulation Solar System and ESS Storage Capacity Optimization

Prepare current data of total energy consumption of building, 160 kW solar system and 50kW ESS. Calculate unutilized solar energy Select a range of potential solar size, 160 (Ref),180, 200, 250 kW. Select a range of potential ESS storage capacities, 0–500 kWh with increments of 25 kWh. Simulation system and evaluate performance by zone

Unutilized Solar Energy (%) Calculation

• Step 1: Identify Net Energy Demand

Calculate the net energy demand at each time step:

$$E_{net}=E_{consumption}-E_{solar}$$

where

• $E_{consumption}$ is the energy consumed by the building.
• $E_{solar}$ is the energy generated by the solar PV system.

If $E\_net$ is negative, it indicates that the solar system is generating more energy than the building consumes.

• Step 2: Charge the Energy Storage System (ESS)

If $E\_net$ is negative and the ESS has available capacity, the excess solar energy can be used to charge the ESS:

$$E_{charge}=\min \left(\left|E_{net}\right|, C_{ESS}-C_{current}\right)$$

where

• $E_{charge}$ is the amount of energy used to charge the ESS.
• $C_{ESS}$ is the total capacity of the ESS.
• $C_{current}$ is the current charge level of the ESS.

This step ensures that any excess solar energy is stored in the ESS until it reaches its capacity limit.

• Step 3: Calculate Unutilized Solar Energy

Unutilized solar energy is the portion of excess solar energy that cannot be stored in the ESS:

$$E_{unutilized}=\left|E_{net}\right|-E_{charge}$$

This value represents the energy that is generated but not utilized by the building or stored in the ESS.

• Step 4: Sum Across Time

Perform the above calculations for each time step (e.g., hourly or daily) and sum up the unutilized solar energy over the entire period:

$$\Sigma E_{unutilized}$$

• Step 5: Calculate Unutilized Solar Energy (%)

Finally, calculate the percentage of unutilized solar energy relative to the total solar energy generated:

$$Unutilized Solar Energy \left(\%\right)=\left({\displaystyle \frac{\Sigma E_{unutilized}}{\Sigma E_{solar}}}\right)\times 100$$

where

• $\sum E_{solar}$ is the total solar energy generated over the entire period.

In the second step of the method, the data presented in the table were collected from the energy usage behavior of appliances within the ERDI building at Chiang Mai University. The data collection process involved monitoring the usage patterns of various appliances during different time periods, categorized into working hours and non-working hours, both during the day and night. The total energy consumption was analyzed by breaking it down into individual appliance contributions, such as HVAC systems, lighting, and electronics, based on predefined percentage ratios derived from historical data. As illustrated in Figure 8, this process included generating usage patterns and classifying appliances. Additionally, Algorithm 2 outlines the behavioral analysis and appliance ratio method used to assign percentage ratios and simulate the system’s performance in different zones. The following table summarizes the behavior of each appliance group and their respective ratios of total energy consumption during working hours in the office building.

Algorithm 2: Behavioral Analysis and Appliance Ratio Method

Record typical building behaviors (e.g., working hours, occupancy). Classify appliances (HVAC, lighting, electronics) and Assign percentage ratios to each category based on historical data. Break down total energy consumption into individual appliances based on predefined ratios. Generate usage patterns for each appliance category reflecting behavior and ratios. Switch to night mode building behaviors consumption scenario Simulation System and Evaluate Performance by Zone

Additionally, simulate nightly energy usage in the building to understand how energy is consumed during non-working hours. Use this simulation to assess the effectiveness of the current ESS and explore optimization strategies. By analyzing the simulated nightly energy consumption patterns, identify the optimal ESS capacity that minimizes energy waste and maximizes energy efficiency, ensuring the building meets its Nearly ZEB Level 1 targets.

2.5. AI Tools

This study utilized OpenAI’s ChatGPT 4o as a language assistance tool for improving the clarity and structure of the manuscript. Specifically, the tool was employed to refine sentence structure, enhance grammar, and ensure overall coherence in the presentation of research findings. No part of the scientific content or analysis was generated by the AI tool; its use was limited to language editing and manuscript organization.

3. Results

This section presents the results of the simulation and analysis performed on the ERDI Chiangmai building’s energy performance, focusing on the optimization of the solar PV system and energy storage system (ESS), as well as the analysis based on behavior and appliance ratios.

3.1. Unutilized Solar Energy

Unutilized solar energy is the portion of solar energy that is generated but not utilized by the building. This occurs when the energy generated by the solar PV system exceeds the building’s consumption, and the excess energy cannot be stored in the ESS due to capacity limitations as clearly shown in Figure 9.

3.2. Energy Performance by Solar PV and ESS Capacity

Current Position in ZEB Zones: With the existing 160 kW solar PV system and 50 kWh ESS, the building achieves an energy usage reduction that places it within the ZEB Ready zone (50–75% energy reduction). The current setup does not yet reach the Nearly ZEB Level 1 zone (87.5–100%) described in Figure 10.

ESS Capacity Enhancement: Increasing the ESS capacity beyond 50 kWh could potentially reduce the percentage of unutilized solar energy, pushing the building’s performance closer to the Nearly ZEB Level 1 zone. For instance, moving from 50 kWh to 150–200 kWh could reduce unutilized solar energy significantly while also increasing the total energy reduction percentage.

3.3. Comparative Simulation of Enhanced Solar PV Systems

The simulation was conducted across a range of solar PV capacities (180 kW, 200 kW, 250 kW) and ESS storage capacities (0 kWh to 500 kWh, in increments of 25 kWh) as demonstrated in Figure 11. The primary metrics analyzed included energy efficiency, unutilized solar energy, and alignment with the Nearly Zero-Energy Building (nZEB) performance zones defined by the SHASE Japan.

The analysis explores how varying solar PV and ESS capacities influence energy usage reduction and unutilized solar energy, highlighting the impact on achieving ZEB performance zones.

3.3.1. Trends Observed across Different Solar PV Capacities

• General Observation: Across all configurations, as ESS storage capacity increases, the total energy usage reduction also increases, while the percentage of unutilized solar energy decreases. The movement from ZEB Oriented Building (35–50%) to Nearly ZEB Level 1 (87.5–100%) zones indicate progressive improvements in energy efficiency as both solar PV and ESS capacities increase.
• 160 kW Solar PV System: Increasing the ESS capacity beyond 50 kWh could potentially reduce the percentage of unutilized solar energy, pushing the building’s performance closer to the Nearly ZEB Level 1 zone, but the current setup does not yet reach the Nearly ZEB Level 1 zone (87.5–100%).
• 180 kW Solar PV System: The energy usage reduction shows improvement compared to the 160kW system, advancing further into the ZEB Ready zone. However, it still falls short of achieving the Nearly ZEB Level 1 zone (87.5–100%), indicating that while performance has improved, additional enhancements in either ESS capacity or PV system size are required to reach the highest energy efficiency levels. Unutilized solar energy decreases faster as ESS capacity increases, indicating better alignment between generation and storage.
• 200 kW Solar PV System: The total energy reduction begins to reach the Nearly ZEB Level 1 zone at ESS capacities around 275 kWh. There is a consistent decrease in unutilized solar energy, making the system significantly more efficient at utilizing the generated solar energy. This configuration represents a more balanced approach, improving both energy efficiency and solar energy utilization compared to lower-capacity systems.
• 250 kW Solar PV System: The 250 kW system is the most efficient in terms of energy usage reduction, achieving Nearly ZEB Level 1 at ESS capacities starting from 175 kWh. Unutilized solar energy is minimized more effectively, even at lower ESS capacities, making this configuration potentially the most balanced and optimal for maximizing energy efficiency and solar energy utilization.

3.3.2. Key Simulation Insights for Optimization

In both cases, unutilized solar energy is effectively minimized, particularly in the 250 kW configuration, which optimally balances solar generation with storage capabilities, as demonstrated in Figure 12.These findings suggest that strategically increasing both solar PV capacity and ESS storage is essential for advancing the building towards the Nearly ZEB Level 1 zone, where energy losses are minimized, and energy self-sufficiency is maximized.

This approach should be a primary focus for building managers and energy planners who aim to achieve the highest levels of energy efficiency and sustainability in Zero Energy Buildings.

In addition to optimizing the building’s energy performance toward Nearly ZEB Level 1, this study assessed the financial feasibility of the proposed solar PV and energy storage system (ESS) configurations through a cost–benefit analysis. By analyzing different system sizes, the financial implications were quantified in terms of payback period and return on investment (ROI), as shown in

Table 1. Cost–benefit analysis of the reference system compared to optimized systems.

System	Energy Reduction	Solar PV Cost (USD)	ESS Cost (USD)	Annual Savings (USD)	Annual Maintenance Cost (USD)	Total Annual Savings (USD)	Payback Period (Years)	ROI (%)
160 kW Solar PV + 50 kWh ESS (Ref)	64.21%	160,000	11,000	22,512	3290	19,222	8.93	9.40%
200 kW Solar PV + 275 kWh ESS	87.62%	200,000	60,500	30,024	5210	24,814	10.50	7.40%
250 kW Solar PV + 175 kWh ESS	88.39%	250,000	38,500	30,348	5770	24,578	11.80	6.88%

.

The solar PV cost is calculated at 1000 USD/kW (including installation) [24] based on the system size, and the ESS cost is calculated at 220 USD/kWh (including installation) [25] according to the specified ESS capacity, reflecting the average retail price in Thailand in 2023–2024 [26,27]. The total cost represents the sum of the solar PV and ESS costs. An annual maintenance cost is estimated at 2% of the total system cost per year. Annual savings, payback period, and ROI are determined based on the building’s estimated daily energy consumption of 800 kWh/day and an electricity grid rate of 0.12 USD/kWh [28].

For the 160 kW PV and 50 kWh ESS system, which delivers an energy reduction of 64.21%, the total cost amounts to 204,500 USD, with annual savings of 22,512 USD, an updated payback period of 8.93 years, an ROI of 9.40%, and an annual maintenance cost of 3290 USD. The 200 kW PV and 275 kWh ESS system achieves a higher energy reduction of 87.62%, with a total cost of 308,750 USD, generating annual savings of 30,024 USD, an updated payback period of 10.50 years, an ROI of 7.40%, and an annual maintenance cost of 5210 USD. Lastly, the 250 kW PV and 175 kWh ESS system provides the highest energy reduction of 88.39%, with a total cost of 343,750 USD, annual savings of 30,348 USD, an updated payback period of 11.80 years, an ROI of 6.88%, and an annual maintenance cost of 5770 USD.

While the payback period for the larger systems, such as the 200 kW PV and 275 kWh ESS (10.50 years) and 250 kW PV and 175 kWh ESS (11.80 years), is longer, these systems are crucial for achieving Nearly ZEB Level 1, where energy consumption is reduced by 87.5% to 100%. Although they involve a higher upfront investment, they align with long-term goals of energy efficiency and sustainability.

3.3.3. Analyze Behavioral Energy Consumption Patterns

The analysis of total energy consumption involves breaking down the data into contributions from individual appliances, such as HVAC systems, lighting, and electronics, based on predefined percentage ratios derived from historical data. This method allows us to transform the daily energy consumption data into patterns that accurately reflect the energy usage of different appliance groups, enabling more targeted energy management strategies.

The behavior and energy consumption ratios of different appliance groups within the office building during a typical working day are summarized in

Table 2. Group of appliance behavior and ratios on working-day office building.

Group of Appliance	Time Period	Behavior	Ratio of Total Consumption
HVAC	08:00–17:00	Gradual rise starting at 08:00, peaking during the day. Increased by 40% from 13:00 to 15:30.	60% base, shift during 13:00–15:30
Indoor Lighting	08:00–17:00	Stable during working hours.	5% during 08:00–17:00
Indoor Lighting (Night)	19:00–06:00	Low usage at night.	0.1% during 19:00–06:00
Outdoor Lighting (Night)	19:00–06:00	Consistent usage at night.	0.25% during 19:00–06:00
Electronics Devices	08:00–17:00	Steady usage during the day.	10% during 08:00–17:00
Electronics Devices (Night)	17:00–08:00	reduced usage at night.	8% during 17:00–08:00
Others	08:00–17:00	Steady usage during the day.	10% during 08:00–17:00
Others (Night)	17:00–08:00	Steady usage during the night.	7% during 17:00–08:00

. The data were collected based on specific time periods, categorizing usage into daytime working hours and night-time non-working hours. The HVAC system, for example, shows a gradual increase in consumption starting at 08:00 a.m., peaking between 13:00 and 16:00 with a 40% increase in usage. Indoor lighting maintains steady usage during working hours, while outdoor lighting and night-time indoor lighting have minimal usage. Electronics and other devices exhibit steady consumption during the day with a slight reduction at night. This detailed breakdown is crucial for understanding how different appliances contribute to overall energy consumption and for optimizing energy management strategies.

The graph illustrated in Figure 13. highlights the energy consumption patterns throughout a typical working day, with noticeable increases during the late morning and early afternoon, peaking around midday. It indicates that energy usage is lowest during the early morning hours and after working hours in the evening, consistent with reduced activity in the building during those times. This detailed daily pattern is crucial for understanding the building’s energy needs and for optimizing energy management strategies as disaggregation shown in Figure 14.

3.3.4. Simulate Appliance Patterns Form Behavioral Data and Appliance Ratios

The switch to nightly building consumption mode is based on appliance ratios and behavioral data, as detailed in

Table 3. Switch mode: Group of appliance behavior and ratios on nightly building testing.

Group of Appliance	Time Period	Behavior	Ratio of Total Consumption
HVAC	00:00–18:00	Shut-off at 0:00–08:00 Gradual rise starting at 08:00–18.00	10% base during 08:00–18:00
HVAC (Night)	18:00–23:30	Gradual rise starting at 18:00	60% base during 18:00–23.30
Indoor Lighting	08:00–17:00	Low usage at daytime.	0.1% during 08:00–17:00
Indoor Lighting (Night)	17:00–01:00	Heavy working usage at night.	10% during 17:00–01:00
Outdoor Lighting (Night)	17:00–01:00	Heavy usage at 17:00–01:00. Low usage at 01:00–07:00.	5% during 17:00–01:00 1% during 01:00–07:00
Electronics Devices	08:00–17:00	Steady usage during the day.	10% during 08:00–17:00
Electronics Devices (Night)	17:00–08:00	Higher usage at 17:00–20:00. peak usage at 20:00–0:00	12% during 17:00–20:00 20% during 20:00–00:00
Others	08:00–17:00	Steady usage during at 00:00–17:00.	10% during 00:00–17:00
Others (Night)	17:00–08:00	Higher usage during the early night.	18% during 17:00–00:00

.

Nightly building consumption patterns are simulated using switch to night mode scenario based on appliance ratios, behavior, and total consumption data, with an assumed total daily energy consumption of 786 kWh. This simulation is conducted with the same 160 kW solar system as the basis. By applying the predefined appliance ratios and considering typical building behaviors during night time hours, the simulation models the contribution of different appliances to the overall energy usage at night, as shown in Figure 15. This analysis helps us to understand how energy is distributed among various appliances and identifies opportunities for optimizing energy management, particularly in reducing consumption during low-activity periods. The findings can be used to refine energy strategies, such as adjusting appliance operation schedules or enhancing the efficiency of the solar system and energy storage usage during the night.

The goal is to identify the point at which the building’s energy efficiency reaches the Nearly ZEB Level 1 zone, characterized by an energy reduction from 87.5% to 100%. As shown in Figure 16, the energy reduction percentage increases as the ESS capacity is expanded. The transition into the Nearly ZEB Level 1 zone occurs at an ESS capacity of 475 kWh. At this point, the energy efficiency of the building has been optimized to a level where nearly all of the solar energy generated is utilized effectively, minimizing energy losses and aligning with the Nearly ZEB Level 1 criteria. This analysis indicates that for a building with substantial night-time energy consumption, a 160 kW solar system paired with a 475 kWh ESS is necessary to achieve the highest energy efficiency standards.

4. Discussion and Conclusions

This study provides a comprehensive analysis of how varying solar generation capacities and energy storage system (ESS) sizes can impact the energy efficiency and overall performance of Zero-Energy Buildings (ZEBs).

Key findings indicate that larger solar generation capacities, particularly those of 200 kW and above, paired with appropriately scaled ESS capacities of 275 kWh and higher, are essential to approach or achieve Nearly ZEB Level 1 status, as illustrated in Figure 17. Specifically, the 200 kW system reaches the Nearly ZEB Level 1 zone with an ESS capacity of approximately 275 kWh, while the 250 kW system achieves Nearly ZEB Level 1 with an ESS capacity starting from 175 kWh. This study also highlights that increasing the ESS capacity significantly enhances the building’s ability to reduce energy usage, transitioning from ZEB Oriented Building zones to ZEB Ready and finally to Nearly ZEB Level 1.

Changing the mode to night activity behavior in the building has an effect on the energy performance of different systems, as demonstrated by the nightly energy usage in a building and the simulated appliance patterns. This figure shows how various appliances, such as HVAC systems, lighting, and electronics, contribute to the overall energy consumption throughout the night. The HVAC system, with its peak consumption during the late evening, plays a crucial role in the building’s energy profile, especially in systems designed for nightly operation. Achieving the Nearly ZEB Level 1 zone (87.5–100% energy reduction) of nightly activity, the simulation is entering the Nearly ZEB Level 1 zone at 475 kWh of solar 160 kW (current system).

Notably, the building achieves the “Nearly ZEB Level 1” classification (87.5–100% energy reduction) when the ESS capacity reaches higher levels, indicating that sufficient energy storage is crucial for maximizing energy efficiency in a building that primarily operates at night. This emphasizes the importance of appropriately scaling ESS capacity to meet the specific energy demands of different building types and usage patterns, ensuring optimal energy performance and progression towards Zero-Energy Building (ZEBs) status.

The findings of this research underscore the critical importance of optimizing both solar photovoltaic (PV) system capacity and energy storage system (ESS) size to achieve the highest levels of energy efficiency in Zero-Energy Buildings (ZEBs). This study offers practical insights into the relationship between ESS capacity and energy reduction performance, demonstrating that as the ESS capacity increases, the building’s energy usage reduction significantly improves. This progression moves from the ZEB Oriented Building zone (35–50% energy reduction) to the ZEB Ready zone (50–75% energy reduction), eventually approaching the Nearly ZEB Level 1 zone (87.5–100% energy reduction).

However, achieving Nearly ZEB Level 1 is not solely about technical optimization; it also requires careful consideration of cost, feasibility, and financial implications. The deployment of larger ESS capacities or more extensive solar PV systems involves significant capital investment. Therefore, a comprehensive cost–benefit analysis is essential to determine the financial viability of these systems [29]. This includes assessing the payback period or break-even point, where the savings in energy costs offset the initial investment. Additionally, scalability must be evaluated, taking into account available space, grid compatibility, and regulatory considerations [30].

While a well-sized solar PV system and an adequately scaled ESS are essential for reaching Nearly ZEB Level 1, the decision to implement these systems should be guided by a detailed financial model. This model should include not only the initial costs but also ongoing maintenance expenses, potential incentives or rebates, and the projected energy savings over the system’s lifetime. Factors such as the building’s location, local temperature variations, solar intensity, and seasonal changes should be considered for optimal system performance [31]. Moreover, the incorporation of alternative renewable energy sources, such as biowaste, could be explored to complement solar energy, particularly in regions with seasonal fluctuations in solar intensity.

Furthermore, these systems provide less dependency on rising energy prices by reducing reliance on grid electricity to under 13%, ensuring more predictable operating costs in the future. Investing in these systems also enhances energy security, offering greater resilience during grid outages and increasing the property’s overall value due to its advanced energy efficiency, stability, and sustainability credentials.

When comparing this optimized system to other energy management system (EMS) techniques, such as IoT-based real-time load scheduling [32] or NILM-based load disaggregation [33,34], several potential improvements emerge. The IoT-based approach excels in demand response and cost reduction through real-time adjustments of load priorities but lacks deeper integration with renewable energy sources like solar PV and ESS. On the other hand, NILM techniques, which focus on disaggregating load consumption at the appliance level using deep neural networks, offer precise monitoring of nonlinear and multi-phase loads but tend to be more reactive rather than predictive.

To improve EMS behavior analysis, integrating Non-Intrusive Load Monitoring (NILM) [35] could provide a more precise understanding of energy consumption at the appliance level, particularly for complex systems like HVAC. This would complement the broader predictive solar and ESS optimizations in the system, enhancing energy efficiency and reducing waste. The combination of predictive energy management with appliance-level disaggregation would offer both real-time control and long-term sustainability, improving overall system performance. Merging AI-driven energy optimization with NILM insights [36] could enable the system to adapt to the building’s specific behaviors, especially in buildings with distinct day and night energy usage patterns. This would make the EMS more capable of achieving nZEB targets, bridging the gap between reactive and predictive energy management.

To further strengthen the system’s accuracy and adaptability, extending the data collection period in future studies is essential. A longer data collection period would allow for a more comprehensive understanding of seasonal variations in energy consumption and solar energy generation [37]. By covering a full year, this extended period would capture seasonal patterns that are critical for buildings with fluctuating energy needs throughout the year.

Moreover, implementing model seasonal adjustments based on historical data would enhance the system’s predictive capabilities. Historical data on weather, temperature fluctuations, and solar irradiance could be used to simulate seasonal variations, even when the data collection period is limited. These adjustments would allow for more accurate forecasting of energy demand and renewable energy availability, making the optimized EMS even more responsive to both short-term and long-term energy needs. Together, these improvements would provide a more robust foundation for optimizing energy usage and ensuring continuous progress toward nZEB targets.