Gap Analysis Survey

Of

Challenges of DER Operation & Data Needs for AI Integration in DER Operations

Introduction and Consent.

About the Survey.

This survey aims to understand the operational challenges encountered in Distributed Energy Resource (DER) systems and assess the data requirements necessary to integrate Artificial Intelligence (AI) into DER operations and management. The information will support workforce development, targeted training, and improved operational performance. Your participation is completely voluntary, and your answers will be kept confidential. There are no right or wrong answers. We want to learn from your experience and perspective.

GEDSI Principles

To ensure that everyone can participate fully, this survey follows UK-PACT's Gender Equality, Disability and Social Inclusion (GEDSI) principles:

You may request this questionnaire in alternative accessibility formats.
If you prefer NOT to complete the survey online, a hard-copy version is available.

Persons with disabilities can receive support to complete the form.

If any question makes you uncomfortable, you may SKIP it or STOP at any time.

Time Required: Approximately 15-20 minutes.

Consent: By continuing with this survey, you consent to participate in this research.

Facilitator Guidance: Enumerators should ensure all respondents are informed about the available accessibility options before beginning the survey. Ask whether the respondent would prefer:

An oral reading of questions for low-literacy participants.
Assistance from a support person, interpreter, or caregiver.
A physical (paper) copy instead of a digital device.

SECTION A: RESPONDENT INFORMATION

This section about the Respondent's information and organizational details

Name of The Organization (optional) ^*

In which state/region do you currently live in Nigeria?

Primary Area of DER Involvement:

Solar PV

Wind Engergy

Energy Storage

Mini/Micro Grids

Energy Management Systems

Other:

Years of Experience in DER Sector:

0–2 years

3–5 years

6–10 years

Over 10 years

Gender:

Male

Female

I prefer not to say

Role in Organisation:

Engineer

Technician

Operations Manager

Data/ICT Staff

Project Manager

Other:

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SECTION B: WASHINGTON GROUP QUESTIONS (WGQS) FOR DISABILITY IDENTIFICATION

SECTION B: DER Operational Challenges

How would you rate the overall difficulty of operating DER systems?

Very high

High

Moderate

Low

Very Low

Which challenges do you frequently encounter in DER operation? (Check all that apply)

Voltage fluctuations

Frequency instability

Weather-related generation variability

Inverter or system faults

Battery degradation or poor performance

Load management issues

Poor network communication

Other:

How often do equipment faults or outages occur?

Very frequently

Frequently

Occasionally

Rarely

Never

How confident are you in your team’s ability to troubleshoot DER system issues?

Very confident

Confident

Moderately confident

Not confident

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SECTION C: Monitoring, Control & System Visibility

How effective is your current DER monitoring system?

Very effective

Effective

Moderately effective

Ineffective

Not available

Which gaps exist in your ability to monitor DER performance? (Check all that apply)

Lack of real-time monitoring

No remote-control capability

Poor data logging

Missing weather data

No asset health monitoring

Other:

How often do monitoring or communication failures affect DER operations?

Very often

Often

Sometimes

Rarely

Never

Other:

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SECTION D: Data Availability & Quality

Which types of DER operational data do you currently collect? (Check all that apply)

Power output data

Irradiance/weather data

Load/consumption data

Inverter logs

Battery SOC/health data

Fault logs

Asset inventory

None

Rate the overall quality of your DER operational data:

Very Low

Low

Moderate

High

Very High

How accessible is DER data for analysis and decision-making?

Very accessible

Accessible

Somewhat accessible

Not accessible

What major barriers affect DER data quality? (Check all that apply)

Missing values

Manual data entry errors

Inconsistent formats

Poor sensor or meter accuracy

Data not collected frequently

Other:

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SECTION E: Data Needs for AI Integration

Which additional data is required to improve AI-based DER analysis? (Check all that apply)

Women (Percentage %):

Detailed irradiance and weather data

Asset health and degradation data

Power quality data

High-resolution generation/load data

DER location and geospatial data

Forecasting datasets

Other:

How prepared is your organisation for AI-driven DER monitoring or forecasting?

Not Prepared

Moderately Prepared

Very Prepared

Which technical barriers limit AI integration into DER operations? (Check all that apply)

Limited digital infrastructure

Poor communication network

Lack of sensors/IoT devices

Insufficient data storage

Low staff capacity

Other:

Which skills are lacking within your team for AI integration? (Check all that apply)

Data analytics

Python/AI modeling

DER monitoring skills

Forecasting and optimization

Data engineering

Cybersecurity

Other:

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SECTION F: Policy, Standards & Support Systems

Which institutional or regulatory barriers hinder AI integration in DER operations? (Check all that apply)

☐ Lack of standard data formats

Insufficient interoperability standards

Lack of incentives for digital innovation

Data privacy restrictions

Limited management support

Other:

What support would improve AI-driven DER management in your organisation?

Better monitoring equipment

Improved data management systems

Technical training

Policy or regulatory guidance

Funding for digital infrastructure

Other:

Q16. From a regulatory/policy perspective, what are the most significant systemic barriers preventing greater participation of women, youth, and persons with disabilities in Nigeria's energy sector workforce?

Rate the significance of each barrier (1=Not significant, 5=Very significant):

Don’t Know

Lack of gender-sensitive energy policies,

Insufficient enforcement of existing anti-discrimination laws.

Limited access to finance for women-led energy businesses.

Educational curriculum gaps in STEM/energy programs.

Cultural and social norms limiting women's participation.

Lack of data and monitoring systems for diversity tracking.

Inadequate coordination between government agencies on GEDSI.

Limited accessibility standards for persons with disabilities.

Weak linkages between industry and educational institutions.

THE END

Thank you for your valuable contribution!

For any questions about this survey, please contact:

info@neat-hub.abu.edu.ng

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