Evaluation of Solar Resource Availability and Smart Load

Scheduling for Residential Buildings

William Olurotimi Falana

, Ikechukwu Samuel Obidi

and Samuel Nii Tackie

Dept. of Electrical and Electronic Engr, Near East University, Nicosia, Cyprus

Dept. of Electronics and Computer Engr, University of Nigeria, Enugu, Nigeria

Keywords: Solar Resources, Smart Load Scheduling, Energy Management, Electric Grid.

Abstract: Residential energy applications based on solar resources are rapidly becoming the norm due to the enormous

advantages of solar energy. In this study we investigated and evaluated the amount of solar resources available

around the year using Cyprus has a case study. This was done by understanding the seasonal trends and

potential solar outputs that can be available to a hypothetical residential load which were classified into two.

Fixed load of 2.5kw and flexible load of 1.5kw making the total load 4kw. To achieve this we used a dataset

from NASA POWER that provides us with important information about our case study Cyprus such has All

Sky Surface Shortwave Downward Irradiance and All sky isolation clearness index which was used for this

analysis. An evaluation model is created using Python to simulate the availability and reliability of solar

energy resources for a potential smart load scheduling strategy using the hypothetical residential load of 4kw.

The results shows that during the summer period there is abundance of solar resources to cater for our

hypothetical residential load of 4kw (fixed and flexible loads), with an average daily energy production of

6.83 kWh and a clearness score of 0.66, this suggest that during this period the sky conditions is perfect for

solar collecting. During the winter period the result suggest that there was less solar resources availability to

cater for our hypothetical residential load of 4kw, with an average daily energy production of 2.46 kWh and

a clearness score of 0.49, suggest that during this period the solar resources can only cater for the fixed load,

this is as a result of frequent cloud cover and limited sun. Spring and autumn indicated moderate levels with

some variation.

1 INTRODUCTION

The energy demands around the world are steadily

increasing, especially for clean, reliable and

sustainable energy (Obaideen et al., 2023), indicating

the need for intelligent and highly efficient energy

management methods and strategies, particularly in

residential buildings. Smart load scheduling involves

methods and strategies used for managing and

prioritizing electrical loads based on availability,

cost, and user preferences (Yang et al., 2023).

Residential buildings mostly have need for systems

and gadgets which are mostly energy dependent and

expensive to run. Sustainable energy sources like

solar photovoltaic systems if properly explored, has

the ability to reduce grid power consumption and save

https://orcid.org/0009-0002-4339-7418

https://orcid.org/0009-0003-6357-7608

https://orcid.org/0000-0002-5141-9974

cost. An example of this optimization technique is

smart load scheduling. This is critical for energy

efficiency, lower energy costs, and grid sustainability.

The availability of Sustainable energy sources,

such as solar energy, is fast becoming increasingly

popular in Cyprus, particularly in residential

applications (Kassem, Gokcekus, & Aljatlawe,

2023). With new innovations in smart grid

technologies and efficient energy management

systems, using solar energy in residential building is

more feasible and effective. (Khan et al., 2022).

Natural events like Seasonal and climatic changes

have posed a major challenge on the solar energy

supply, leading to solar energy's fluctuation which is

a significant problem in maximizing its usage for load

scheduling. Smart load scheduling is the process of

Falana, W. O., Obidi, I. S. and Tackie, S. N.

Evaluation of Solar Resource Availability and Smart Load Scheduling for Residential Buildings.

DOI: 10.5220/0014288600004848

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences (ICEEECS 2025), pages 203-210

ISBN: 978-989-758-783-2

203

strategic management of energy resources. In the case

of solar resources, it is the efficient management of

energy consumption in accordance with periods of

maximum solar energy availability (Dragomir &

Dragomir, 2023). For effective load scheduling, it is

critical to understand the patterns of solar irradiance

and their implications on energy generation. Making

use of dataset like that of NASA’s power for the

Prediction of Worldwide Energy Resources data

(NASA, 2025), helps to provide a dependable method

for analysing solar resource availability in Cyprus as

a case study. In this study, our goal is to evaluate the

seasonal changes in solar resources using NASA

power dataset to estimate the monthly energy output

by conducting an analysis using metrics like all-sky

surface shortwave downward irradiance and all-sky

isolation clearness index. This study is intended to

support and provide useful and important insight to

homeowners, planners, and policymakers in making

informed decisions on the usage of solar resources

and grid reliance throughout the year.

2 LITERATURE REVIEW

Smart load scheduling and solar resource availability

are very important for efficient energy usage,

especially in applications related to solar energy

generation. With respect to this, there have been

several related works on smart load scheduling

strategies that have been considered in several

instances and solar resource evaluations.

Chreim et al introduce a price-based demand

response system for residential smart homes that

combines renewable energy, battery storage, and

electric cars. It employs a hybrid algorithm for

optimal load scheduling and a machine learning-

based clustering technique to learn user preferences

from real-world consumption data. The result was

tested using actual smart house traces and put on a

Raspberry Pi to assess performance and energy

consumption. (Chreim et al., 2022).

Remani et al provide an average home load

scheduling model that incorporates renewable energy

sources such as PV into any tariff structure. It

proposes a reinforcement learning (RL)-based

strategy for managing load commitment under

uncertainty while retaining customer satisfaction.

(Remani et al., 2018).

Chen et al describe a demand response scheduling

strategy for residential buildings that aims at four

types of loads, including air conditioning and other

deferrable/interruptible categories. It uses the

Nondominated Sorting Genetic Algorithm II to

balance power costs and user discomfort. The method

is evaluated on an ASHRAE 140 standard building

under both working and nonworking day conditions.

The results indicate successful peak load shifting,

lower power expenses, and sustained occupant

comfort (Chen et al., 2022).

Albogamy et al proposed an (EMC) that uses a

hybrid Enhanced Differential Evolution and Genetic

Algorithm (EDGE) to automate home load

scheduling. The EMC responds to demand response

signals by managing three types of home loads:

interruptible, non-interruptible, and hybrid.

Simulation findings demonstrate that EDGE

outperforms current algorithms such as BPSO, GA,

WDO, and EDE across all performance measures

(Albogamy et al., 2022).

Ikram et al Investigated Two meta-heuristic

optimization strategies for scheduling flexible

household loads in a smart home equipped with

rooftop solar, battery storage, and grid connectivity.

The goal is to cut power bills and peak-to-average

ratios while keeping users comfortable. The

simulation findings reveal a 4.5% decrease in daily

power costs from 507.12 to 484.33 BDT,

demonstrating the effectiveness of both optimization

strategies without shutting off necessary loads (Ikram

et al., 2024).

Abdelhameed et al present smart home load

scheduling as a multi-objective restricted mixed-

integer optimization problem (CP-MIP) for lowering

power costs and increasing user comfort. The strategy

is evaluated using time-of-use pricing and two power

modes: grid-only and grid-tied PV. Four

metaheuristic algorithms are compared, including

CL-JAYA and SOH-PSO. The results demonstrate

considerable bill savings (up to 56.1%), with CL-

JAYA delivering the best user comfort

(Abdelhameed et al., 2023).

Stroia et al present a networked sensor system for

real-time monitoring and forecasting of domestic

appliance power usage and ambient variables. It

enables load modelling, database building, and

testing of load-scheduling algorithms at various sizes,

ranging from single residences to large cities. A

hardware/software co-designed architecture

combines building automation and energy

management technologies. The use of piecewise

linear (PWL) load profile representations is proven to

enhance peak shaving compared to standard average-

based techniques (Stroia et al., 2022).

Qayyum et al investigation looks into the

integration of energy management systems in smart

residential structures as key components of smart

cities. It examines the relationship between smart

ICEEECS 2025 - International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences

204

grids, energy storage, infrastructure, and urban

sustainability without using mathematical models.

The study discusses how developing energy sources

and efficient transportation affect smart urban

systems, emphasizing the importance of cross-

disciplinary, holistic methods. (Qayyum et al., 2023).

Tackie & Özerdem evaluated the performance of

the 1.275 MW Kib-Tek solar power facility in

Northern Cyprus. It evaluates plant efficiency,

capacity factor (17.71%), and performance ratio

(85.77%) using PVsyst simulations. The addition of a

single-axis tracker decreased the payback period from

nine to seven and increased production by 27.88%. At

the simulated locations of Famagusta, Girne, and

Lefkosa, grid-injected energy increased by 31.22%

with tracking (Tackie & Özerdem, 2022).

Ozerdem et al. evaluated the performance of the

1.2 MW Serhatkoy PV power plant, the first grid-

connected plant in North Cyprus. This study

simulation was used to determine important

parameters, including performance ratio (PR) and

capacity factor (CF), using PVSyst software and

NASA weather data. In order to facilitate future

development, maintenance planning, and investment

evaluation, the payback period is also approximated,

taking currency exchange rates into account

(Ozerdem et al., 2015).

3 ANALYTICAL FRAMEWORK

3.1 Data Overview

The Goal of this evaluation is to study the availability

and reliability of solar energy resources in Cyprus

using NASA POWER satellite-derived data (NASA,

2025). And to evaluate the possibility of adopting

intelligent load scheduling systems in residential

buildings. Furthermore, to estimate solar energy

output and analyse its temporal trends (daily,

monthly, and seasonal). In regard to this, the dataset

covers the period of 1st January 2019 to December

31st 2024, which contains the following:

• All Sky Surface Shortwave Downward

Irradiance (kWh/m²/day): This contains the

total amount of solar irradiance information

that reached the earth irrespective of

different sky conditions.

• All sky isolation clearness index: This

contains the ratio of real solar irradiance to

theoretical clear sky irradiance, which

indicates cloudiness.

3.2 Seasonal Classification

To study seasonal patterns in solar irradiance and

energy output, each record was labelled as winter,

spring, summer, and fall for each month.

3.3 Solar Resources Analysis

To carry out this study, the daily solar irradiance data

are analysed to calculate monthly averages for solar

irradiance and clearness index. Classify days as

Sunny, Partly Cloudy, or Cloudy using clearness

index criteria (≥0.8 sunny, 0.5 ≤ CI < 0.8 partly

cloudy, <0.5 cloudy).

3.4 Solar Energy Output Estimation

The goal is to estimate the solar energy output for a

hypothetical household photovoltaic system. This is

achieved by using the site-specific environmental

data and panel characteristics such as the following

parameters:

Panel area A = 6.4 m²

Panel efficiency μ=18%

Performance ratio PR = 0.8 (accounting for system

losses)

The daily energy output was calculated as:

E daily = A x μ x PR x Irradiance......... (1)

These parameters were considered under standard

conditions.

Panel Area

For a residential building in Cyprus

A normal solar panel = 1.63 m² (Mokhtara et al.,

2019)

A residential building in this hypothetical simulation

has an estimated number of 4 solar panels.

Panel area = 1.63 m² x 4 = 6.52 (rounded up to 6.5)

Panel efficiency

Considering the type of solar panel in this case, we

considered a modern monocrystalline panel, whose

efficiency of converting sunlight into electricity is

around 18 – 22%. We selected the least case for this,

which is 18% (Vodapally & Ali, 2022).

Performance ratio PR

We considered a PR value as a case of a standard and

a well-maintained solar panel, which is 0.8 in this

case.

Scenario 1:

Evaluation of Solar Resource Availability and Smart Load Scheduling for Residential Buildings

205

To estimate the solar energy output for the 2

day of

January 2019.

Panel area A = 6.5 m²

Panel efficiency μ=18%

Performance ratio PR = 0.8

According to the dataset, the all-sky surface

shortwave downward irradiance for the 2

day of Jan

2019 is estimated to be 1.17 kWh/m²/day

E daily = A x μ x PR x Irradiance

E daily = 6.5 m² x 0.18 x 0.8 x 1.17 = 1.095 kWh.

The total energy generated on the 2

day of January

2019 is 1.095 kWh.

3.5 Smart Load Scheduling Model

To facilitate smart load scheduling, residential energy

demand was estimated and divided into two

categories: fixed loads and flexible loads. In a

hypothetical situation, fixed loads were projected to

consume around 1.5 kWh/day, while flexible loads

consumed 2.5 kWh/day, for a total daily consumption

of 4.0 kWh. The identified peak sun hours were

projected to yield 40% of the total daily solar energy.

The purpose of the scheduling model is to determine

how much of a household's electricity use could be

covered by solar energy during the four hours of the

day when sunshine is at its strongest. Additionally, it

examined how much electricity could be saved from

the grid by relocating flexible appliances (such as

water heaters or washing machines) to run during

those sunny hours. Through the use of daily data on

solar energy production and household power usage,

the model assisted in assessing the monthly

effectiveness of a potential smart load scheduling

strategy to improve solar energy usage and lessen the

load’s reliance on the grid.

Grid Energy Savings (kWh): The amount of energy

saved by using solar to replace grid usage.

Peak Grid consumption reduction (kW): An

estimated decrease in peak electricity consumption

based on a 4-hour peak period.

3.6 Average Percentage of Load Met by

Solar

The average percentage of load met by solar metric

indicates how much of a household's or building’s

energy demand could be catered for by solar energy,

often during peak sunshine hours.

Figure 1: Average Percentage of Load Met by Solar.

From Fig. 1, the data reveal that solar energy accounts

for roughly 47% of the home load during peak hours.

Solar energy makes a significant contribution, but

does not entirely meet energy demand with present

system characteristics (PV size = 6.5 m², efficiency =

18%, PR = 0.8). A 47% solar contribution shows the

possibilities for partial load scheduling or battery

integration. To attain more independence from grid

power, expanding PV capacity, enhancing system

efficiency, or optimizing flexible load scheduling

may be put into consideration.

Scenario 2:

From Scenario 1 on the 2

of January 2019, the total

energy generated is 1.095 kWh of electric energy.

We have a fixed load of 1.5kwh/day

And a flexible load of 2.5kwh/day

Total load = 4kwh/day

Total energy generated on the 2

of January 2019 =

1.095 kWh

Grid Energy Savings (kWh) = Total energy generated

on day 2 = 1.095 kWh

During the Peak solar hours, it was anticipated to

generate 40% of the daily solar energy

40% of daily solar energy = 438wh.

Peak Grid Consumption Reduction (kW) = an

estimated decrease in peak electricity consumption

based on a 4-hour peak period.

= 438wh/4h = 109.5 watt.

ICEEECS 2025 - International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences

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Average percentage of load met by solar on 2nd of

Jan 2019 = Total energy generated on 2nd of Jan 2019

/ Total load energy x 100% = 27.3%.

Average percentage of load met by solar on 2nd of

Jan 2019 at Peak solar hours = Solar Energy during 4-

hour peak (kWh)/ load during the 4-hour peak period

x 100%

Assume all 4 kWh is available during the 4-hour

peak period

Load during 4 hours peak period = 4.000kwh/4h

=1000watt

Average percentage of load met by solar =

109.5/1000 x 100 = 10.95%

4 EVALUATION OUTCOMES

4.1 Monthly Average Solar Irradiance

The monthly average solar irradiance graph

(Irradiance vs. Month) is an important tool for

accessing solar energy potential throughout the year.

Figure 2: Monthly average solar irradiance.

• Seasonal Trends

From Fig. 2, it is observed that there is higher

irradiance between March and October,

corresponding to Cyprus's late spring in March,

summer, and early fall. This is due to the fact that

between these months, there are often clearer skies.

• Solar planning insights:

From Fig. 2, it is observed that months with the

highest irradiation, which are between June to

August, are the best for solar PV performance. This

helps us to anticipate system performance,

particularly for off-grid or hybrid solar systems.

• Yearly Comparison

From Fig. 2, it is observed that there is consistency

and slight variation from 2019 to 2024 solar resource,

which further suggests reliable energy yield planning.

4.2 Estimated Daily Solar Energy

Output

The Estimated Daily Solar Energy Output graph is an

important outcome of this study because it converts

solar irradiance into real usable energy (in kWh) that

a solar photovoltaic (PV) system would produce

daily.

Figure 3: Estimated daily solar energy output.

• Daily Variability:

From Fig. 3, Peaks indicate clear, bright days with a

high solar output, while the dips indicate cloudy or

rainy days, or periods with reduced irradiance. From

2019 to 2024, we estimated daily solar energy output

for 2025, taking the average of irradiance from 2019

to 2024 for each day.

• Seasonal Performance:

From Fig. 3, it is observed that during the summer

season, between June to August, output is high and

consistent. During winter seasons, output drops due

to cloud cover.

• Energy Forecasting:

From this information, it is easier to forecast how

much energy you can expect daily/monthly, which is

important for battery size, load planning, and

determining grid backup requirements.

• Supports Smart Load Scheduling:

From this information, it helps indicate when flexible

appliances can be booked on peak solar generating

days.

Evaluation of Solar Resource Availability and Smart Load Scheduling for Residential Buildings

207

4.3 Monthly Grid Energy Savings and

Demand Reduction

From the estimated daily solar energy consumption,

we are able to calculate how much grid electricity can

be saved each month by using solar power during

peak hours, as well as how much this solar usage

decreases the grid's maximum demand.

Table 1: Monthly Grid Energy Savings and Demand

Reduction for Jan – May 2019.

Year Month

Grid

Energy_Savings

(kWh)

Peak Grid

Demand

Reduction (kW)

2019 Jan 25.496 0.205

2019 Feb 33.418 0.298

2019 Mar 52.779 0.425

2019 Apr 64.126 0.534

2019 May 75.696 0.610

The monthly grid energy savings and demand

reduction statistics show how solar PV integration

reduces dependency on traditional grid electricity.

For example, in January 2019, solar power reduced

roughly 25.5 kWh of energy that would otherwise

have come from the grid, resulting in a 0.21 kW

reduction in peak demand. This shows the ability for

solar energy to not only lower monthly energy bills

but also reduce too much load on the power grid

during peak demand hours, contributing to more

reliable and sustainable energy systems.

4.4 Irradiance vs Load

The goal here is to determine if the solar irradiation

on each day is adequate to fulfil a household's energy

requirements, especially during peak seasons.

Figure 4: Irradiance vs load.

From Fig. 4, the irradiance versus load graph shows

how solar resource availability varies over time in

relation to residential energy consumption. The curve

steadily grows, reaches a peak (summer), and finally

decreases (winter). The red dashed line represents a

fixed value, which is the daily energy requirement of

the hypothetical household or system.

Above the red line shows the months in, solar

irradiation meets or exceeds demand, allowing the

building to be self-sufficient or even create extra

energy. Below the red line shows months that solar

irradiation is not enough, necessitating grid help to

satisfy electricity demand.

• Energy Surplus Periods: the period when

irradiance exceeds load suggests the

possibility of grid export, battery charging,

or load scheduling.

• Insufficient Periods: the period when

irradiance falls below demand, during this

period there is grid reliance, backup

generators, or load prioritizing becomes

more necessary.

4.5 Average Monthly Solar Irradiance

The average monthly solar irradiance is the amount

of solar energy received per square meter per day in

each month. This helps in sizing solar panels,

predicting seasonal energy output, and designing

smart load scheduling systems.

Figure 5: Average monthly solar irradiance.

The average monthly solar irradiance in Cyprus

shows a unique seasonal trend, with peak values

observed during the summer season (June–August)

and a noticeable dip during the winter season

(September - May). These seasonal changes and

variations are very important for optimizing solar

power system performance and load scheduling

methods in residential buildings.

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4.6 Seasonal Solar Energy Summary

Seasonal Solar Energy Summary shows how solar

energy availability and sky conditions fluctuate

throughout the year. This includes:

Mean Daily Energy Output (kWh): is defined has the

average quantity of solar energy gathered each day

during each season.

Maximum and Minimum Energy: The best and worst

daily performance recorded throughout that season.

All-sky isolation clearness index is a measure of sky

clarity (near 1 = clear skies, < 0.5 = hazy).

Table 2: Seasonal Solar Energy Summary.

Season

Daily

Energy

Kwh

Mean

Max

Min

Clearness

Index

Fall 3.98 6.14 0.94 0.59

Sprin

5.50 7.76 0.73 0.59

Summe

6.83 7.92 4.37 0.66

Winte

2.46 4.82 0.53 0.48

• Daily energy (kWh)

From Table 2, summer has the highest mean daily

solar energy (6.83 kWh), with the lowest difference

between minimum and maximum values, showing a

consistent excellent solar performance. Winter has

the lowest average (2.46 kWh), with greater

unpredictability and less dependable solar energy

output. Spring and autumn are transitional seasons,

with modest sun availability.

• Clearness Index (CI)

The CI is the ratio of real solar radiation to clear-sky

radiation (0–1). Higher readings (~0.66 in summer)

indicate a cleaner sky and improved sun conditions.

Lower values (~0.49 in winter) imply cloudier

circumstances and more atmospheric interference.

5 CONCLUSIONS

This study used NASA POWER dataset from 2019 to

2024 to assess solar resource availability and its

implications for smart load scheduling in Cyprus

residential buildings. There were several Key

characteristics that are were considered which were

daily solar irradiance, clearness index, predicted solar

energy output, and the extent to which solar energy

can balance grid demand during peak solar hours. The

investigation indicates that the monthly average solar

irradiation is high enough throughout the year to

sustain dependable photovoltaic (PV) power. Daily

solar energy output (based on a 6.5 m² panel at 18%

efficiency and a 0.8 performance ratio) ranged from

2.5 to 6.1 kWh/day, with greater performers seen

during the summer season. During peak sunshine

hours, solar output met 49% of the home load (which

included both fixed and flexible components). This

implies a high potential for load-shifting solutions,

particularly for flexible appliances or battery

charging. The irradiance vs. load study revealed that

solar generation outperformed basic home

consumption levels for several months, particularly

during the summer season. The monthly grid energy

savings and peak demand reduction estimations

indicate that solar integration can considerably

decrease strain on the national grid infrastructure. In

months with high irradiance, flexible loads might be

totally powered by solar energy, saving money and

improving grid dependability.

The seasonal solar energy summary showed strong

trends:

• Summer is the best time to schedule big or

important solar-dependent loads due to high

irradiance and reasonably predictable

weather patterns.

• Winter poses problems, with lower

irradiance levels and more fluctuation,

demanding a larger dependence on grid

assistance or energy storage options.

• Spring and fall offer intermediate conditions

in which partial battery storage or adaptive

scheduling may be most effective in

ensuring energy dependability.

The result of this study demonstrates the potential of

solar-assisted smart load scheduling as a means of

ensuring sustainable energy access, particularly in

Cyprus. Energy planners are advised to use these

seasonal insights when developing demand-side

management programs, home solar incentives, or

hybrid solar-grid systems to increase energy reliance.

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