Building intelligence for Indonesia's electrical grid.
I'm an energy systems professional with 10+ years in Indonesia's electricity sector, specializing in power distribution, network reliability, and efficiency improvement — including technical and non-technical loss reduction.
My experience spans both a state-owned utility (PT PLN) and the private sector (ENGIE), combining operational leadership with applied work in energy efficiency, AI/data analytics, and the green-energy transition. I coordinate multi-stakeholder programmes across government bodies, industry, universities, and local authorities — and work fluently in Bahasa Indonesia and English.
I hold a Ph.D. in Electrical Engineering (Power Systems) from Universitas Indonesia and am pursuing a second Ph.D. at Monash University under the RACE for 2030 programme (urban design & the energy transition). I also founded EnVisor, an AI-powered energy-efficiency advisor, and Electra Academy (electraacademy.com), a practitioner-focused electrical & energy skills academy.
What excites me — the energy transition. I'm an enthusiast of smart grids, distributed solar & net metering, demand-side management, dynamic tariffs, AI for utilities, and turning idle energy into value. I'm actively looking for challenges in advising the energy transition, and love connecting with researchers, engineers, and founders building Indonesia's clean-energy future — if that's you, let's talk.
Building models end to end — from feature engineering to deployment — for detection, ranking, and forecasting.
Modelling and analysing distribution networks, reliability, and load behaviour for planning and operations.
Spatial analysis of electrical infrastructure using night-lights imagery and web mapping.
Shipping production tools and dashboards that operators and communities actually use.
IELTS 6.5 — British Council, 2025
Electricity Usage Enforcement Supervisor — ESDM, 2025
Asst. Manager, Distribution-to-End-User Services — ESDM, 2025
Asst. Manager, Low-Voltage Power Distribution — ESDM, 2022
PT PLN (Persero)
Engie
Universitas Indonesia
IEEE
Australia AwardsFounded Electra Academy — a practitioner-focused electrical & energy skills academy training Indonesia's next generation of engineers with industry-grade, hands-on courses in power distribution, renewable energy, and applied AI.
Built an AI energy-efficiency advisor for Indonesian households — photo-based appliance recognition, monthly-bill estimation, benchmarking, and cost-optimisation advice. Extended to industry via reactive-power / capacitor-bank consulting to cut kVARh penalties.
Safeguard distribution-network efficiency and revenue by protecting energy-transaction integrity and leading enforcement against illegal usage. Manage metering-integrity and field-inspection teams, and built a machine-learning electricity fraud detection system that prioritises inspection targets — raising the detection hit rate from ~15% to ~77% and recovering lost revenue.
Ran a service unit delivering electricity to 300,000+ customers. Oversaw distribution operations, service quality, and rapid complaint resolution; led customer-service and field teams and coordinated with local government and community stakeholders to hold SAIDI/SAIFI reliability targets.
Led the distribution-operations team in a dense industrial area — planning preventive maintenance, coordinating fault response and network switching, and driving reliability improvements to keep SAIDI/SAIFI low across the feeders.
Analysed generation performance across operations, maintenance, and procurement at the Paiton power plant — producing efficiency, reliability, and cost-optimisation insights. My first exposure to private-sector, IPP-scale power generation.
Second doctorate under the RACE for 2030 programme (Reliable, Affordable, Clean Energy) — researching sustainable urban design and the energy transition. Researcher profile on racefor2030.com.au.
GPA 3.85 / 4.00 · Doctoral research in power systems — machine learning for non-technical loss detection, satellite/GIS-based asset inventory, and energy-to-value monetisation (EnerBit).
GPA 3.76 / 4.00 · Power Systems & Machine Learning · Supervisor: Prof. Iwa Garniwa.
GPA 3.48 / 4.00 · Foundation in electrical engineering — power systems, electrical machines, and distribution networks.
My research interests, technical skills, international exposure, and professional memberships — each explained in detail and linked to the publications behind them.
Explore my expertise & interestsResearch interests I'm building on and open ideas I like to discuss — sorted into Energy, Economy, and AI.
Machine learning across the grid — anomaly & loss detection, complaint prediction, and operational forecasting.
ExploreCatching electricity theft with ML ranking + smart meters — lifting inspection hit rate from ~15% to ~77% at PLN.
ExploreConnected metering (STM32 + GPS/GPRS) and turning AMR/AMI data into real-time distribution analytics.
ExploreBenchmarking medium-term demand-forecasting methods for practical distribution planning.
ExploreThe economics and policy of rooftop and industrial solar — adoption drivers and optimal PV placement.
ExploreTariff design for prosumers and industry — Time-of-Use scenarios worth ≈ Rp 3.6 trillion/year in savings.
ExploreShaping demand and integrating renewables — the policy levers that make the energy transition affordable.
ExploreTurning idle power surplus into value — the EnerBit model mapping ~7 GW of spare capacity to a potential ~56,640 BTC/year.
ExploreThe current 1:0.65 export ratio penalizes industrial prosumers while grid infrastructure remains underprepared for bidirectional flow. My 21 MW Bekasi case showed a cumulative consumer loss of −$2,102 over two years. Indonesia needs dynamic net metering — not a static punitive multiplier.
Indonesia has 40% of the world's geothermal potential but uses less than 10%. Paired with the right monetization model, its baseload stability could transform PLN's finances. EnerBit explores one path: regulated Bitcoin mining as intermediate value capture while renewable buildout continues.
30 industrial customers in PLN Bekasi showed mid-peak demand elasticity of −0.3 — they will shift load when the incentive is real. That's a 2.59% daily generation-cost reduction across the interconnection. Dynamic pricing needs political will more than technology.
Countries with unstable fiat aren't using Bitcoin as a medium of exchange; they use it as a hedge against monetary-policy failure. Indonesia's position is stronger, but the principle holds: states that hold Bitcoin early may gain structural advantages. El Salvador and Bhutan are running the experiment.
Classical scholars banned guaranteed returns on capital because they distort risk distribution. Bitcoin, a fixed-supply asset with no guaranteed yield, aligns more naturally with Islamic economic principles than fractional-reserve banking. The debate is about what unit best represents productive participation without exploitation.
Electricity subsidies exceed Rp 70 trillion annually, disproportionately benefiting wealthier, higher-consumption households. A well-designed TOU + targeted-subsidy system would be more equitable and fiscally sustainable. The politics are hard — but so is the cost of inaction as the global transition accelerates.
Every digitalization wave builds better visualizations but never closes the loop to action. An operator sees a loss anomaly — then what? The next frontier is agentic AI that detects the anomaly, cross-references history, generates a work order, and notifies the field team, with no human middle step.
When an AI system makes a wrong recommendation that causes operational loss, who is responsible — the engineer, the vendor, or the system? Until there are clear accountability frameworks for government-linked companies, adoption stays slow regardless of capability. It's a governance problem before it's a technology problem.
Moving a fraud-detection hit rate from 15% to 77% wasn't about a brilliant model — it was because inspectors work from intuition and social pressure, while the model works from patterns. ML doesn't get tired, know the customer, or fear conflict. AI wins most where human judgment is biased by relationships.
Peer-reviewed research on power systems, solar economics, load forecasting, and electricity-theft detection — scroll the papers on the left and the panel on the right shows where each one landed.
Finds where and how much solar to place on a radial feeder to minimise losses — a practical siting guide for distribution planners deploying PV cost-effectively.
Read the paperEvaluates the return on PV at its optimum capacity — helps utilities and investors judge when distributed solar actually pays off.
Read the paperShows how project value shifts with tariff, IRR and CapEx — a risk map for financing solar under uncertainty.
Read the paperBenchmarks statistical vs neural-network forecasting for tropical grids — guides planners in choosing the right demand-forecast method.
Read the paperLinks who adopts rooftop solar to income, HDI and consumption — evidence for targeted incentives and more equitable energy policy.
Read the paperA tamper-aware smart meter (STM32 + GPS/GPRS) that flags theft — helps utilities cut non-technical losses and recover revenue.
Read the paperModels Time-of-Use tariffs for industry to shave peak load — a lever for cheaper generation and a flatter demand curve.
Read the paperHis doctoral work — a field-validated ML model catching electricity theft on non-smart grids, ready to deploy at PLN today.
Read the paperRecovers waste heat from diesel-engine coolant to power micro-hydro — a circular-energy idea for off-grid and remote sites.
Read the paperAssesses net-metering policy for medium-voltage industry — quantifies the cost impact on prosumers and the grid.
Read the paperProduction tools shipped for PLN operations, energy analytics, and the community — all live.
A practitioner-focused electrical & energy skills academy — hands-on courses in power distribution, renewable energy, and applied AI for Indonesia's next generation of engineers.
Satellite street-light detection using NASA VIIRS night-lights + YOLOv8 — cutting field-survey effort by ~70% for public-lighting inventory.
Real-time distribution analytics on AMR data with an automatic loss-anomaly alert engine for PLN operations teams.
Multi-agent operations platform — Claude API + Python with auto-reporting and a Telegram bot packing 18 features.
AI energy advisor for SMEs & commercial buildings — PLN tariff-aware automated audits and clear savings insights.
A spaced-repetition platform for learning Qur'anic Arabic vocabulary, word by word.
Computer-vision football analytics — player detection and tracking with YOLOv8 + ByteTrack.
A regional data platform surfacing local statistics and public information for Indramayu.
A focused Pomodoro productivity timer for deep-work sessions.
A tennis community & tournament platform for players across Indramayu.
Built as part of the Esri Fellowship 2026 — maps, data, and narrative woven together on ArcGIS StoryMaps. Take a look right here — scroll inside the frame, or open the full interactive experience.
Research programmes, awards, conferences, teaching & community work — scroll through the story on the left and the picture on the right keeps up with you.
Indonesia has 4,892 public EV chargers. Vietnam has around 150,000; China has over 5 million. The 2030 target is 62,000 — twelve times what was built in the last decade, in four years. ChargeChain is a funding model for that build-out based on Real-World Asset tokenisation: ownership of a charging asset is divided into fractions so many parties can fund it instead of one large balance sheet, and revenue from EV users flows back through smart contracts — transparently, automatically, and as a share of actual operating income rather than a promised fixed return. A concept prototype for discussion and research, not an investment offering.
Concept & simulation
PLN and grid partners remain the backbone; operators still run the sites. ChargeChain only addresses the layer that has been hardest to move — the capital structure — connecting capital raising, an SPV that owns the charging assets, the O&M partner and EV users through one transparent settlement layer. One detail matters more than it sounds: utilisation data becomes open. China built 5 million chargers and now runs them below 10% utilisation, against a break-even of about 8.5%. Volume without data produces idle assets — a mistake Indonesia cannot afford.
Concept & simulation
Where should the next charging station go, and who has been left unserved? The answer is not in the transaction record: session data is not a map of demand, it is a map of the supply that came first. Areas without chargers record zero sessions — not because the need is absent, but because it was never given anywhere to appear. GeoCharge separates the two. Drawing on 101,020 charging sessions across 636 units in West Java, it builds a Spatial Suitability Index from four factors — predicted demand, POI density, population, and distance to existing stations — and returns 244 candidate locations in ranked build order.
Open the Story Map
Hot spot analysis with Getis-Ord Gi* identified where measured charging energy clusters rather than merely appears. Forest-based Classification and Regression predicted demand into cells with no station history at all — the GeoAI capability that made the latent-demand argument possible. Weighted Suitability Modeling composed the four factors into a single index, and network-based drive-time service areas replaced the Euclidean buffers I started with, which changed the answer materially. Python and the ArcGIS API handled the transaction pipeline; the Maps SDK carried the result into an interactive web layer.
Open the Story Map
150 of the 244 recommended sites (61%) lie outside every 10-minute drive-time service area of an operating station — the portfolio is mostly gap-filling — while 94 sites (39%) densify areas already served where measured demand is concentrated. Every card carries its own evidence: suitability score, measured demand, projected energy and sessions, recommended sizing, and the distance to the nearest operating station. GIS turned out to be a decision instrument rather than a visualisation tool: a good map is one whose argument can withstand scrutiny.
Open the Story Map
Receiving the scholarship award at the celebration of the Ariel Liebman RACE for 2030 Monash University, Indonesia Ph.D. Program — nine fully funded doctorates for Indonesians who have contributed to the country’s energy sector, backed jointly by Monash University Indonesia, the RACE for 2030 Cooperative Research Centre and PT PLN (Persero). The programme is named in honour of the late Professor Ariel Liebman, Director of the Monash Energy Institute, whose vision for Australia–Indonesia energy collaboration shaped it.
My doctoral research poster — “Pathways to Advancing Sustainable Transportation: A Focus on Indonesia’s EV Ecosystem” — carried out under the RACE for EVs program and supervised by Dr. Alyas Widita and Dr. Liz Taylor at Monash Art, Design and Architecture. The work looks at what Indonesia’s charging infrastructure, electricity tariffs and urban form need to become for electric vehicles to scale.
With fellow awardees at the celebration of the Ariel Liebman RACE for 2030 Monash Indonesia Ph.D. Program — Indonesian energy practitioners taking up fully funded doctorates across the country’s energy transition, from grids and tariffs to transport and demand.
The programme’s celebration dinner, bringing the scholarship recipients together with leadership from Monash University, RACE for 2030 and PT PLN to mark the beginning of the cohort’s doctoral journey.
The scholarship cohort together — engineers and practitioners drawn from across Indonesia’s energy sector, brought into one research programme under Monash supervision with RACE for 2030 and PLN funding.
RACE for 2030 is Australia’s industry-led Cooperative Research Centre for Reliable, Affordable, Clean Energy — a ten-year programme launched in 2020 with up to A$350M of resources, including A$68.5M of Commonwealth funding, making it the largest bid in the history of the CRC Program. Its mission is to cut energy bills and carbon emissions, working across five research programs: business energy productivity, new consumer energy technologies, grid optimisation through distributed energy resources, electric-vehicle integration, and foresighting, training & capacity building. My second doctorate is carried out inside this programme.
My RACE for 2030 profile
PLN West Java Innovation Champion (2025) and 3rd place at the PLN National Innovation Awards (2024). CERDAS is a machine-learning complaint-prediction system that reads customer-report keywords to pre-empt service issues; the national award recognised an ML electricity fraud-detection system that lifted the inspection hit rate from ~15% to ~77%.
Featured presenter at the UI Electrical Engineering Friday Morning Forum (Series 55), presenting doctoral research on a hybrid machine-learning model for detecting electricity theft on non-smart-grid networks with multidimensional analysis — under the Smart Grid Research Group (SGRG).
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Recognised as an outstanding participant at the PLN (BUMN) Leadership Awards 2022 — theme “Leadership, Together” — for completing the PLN Leadership Development Programme. Also 3rd Winner of the PLN English Olympiads (2021).
Awardee of the Australia Awards Waste-to-Energy Short Term Award, delivered with Griffith University — studying how waste streams can be converted into clean, reliable power for the energy transition.
Presented the peer-reviewed paper “Anti-Theft Power Smart Metering System” at the IEEE ICT-PEP conference — an STM32 + STPM10 metering design with GPS/GPRS telemetry built to reduce non-technical losses at PLN.
Awarded a Certificate of Presentation for an excellent oral presentation of “Economic Analysis of PV Distributed-Generation Investment Based on Optimum Capacity for Power-Loss Reduction” at the 5th International Conference on Power and Energy Systems Engineering.
Delivering the oral presentation of “Economic Analysis of PV Distributed-Generation Investment” at CPESE 2018 in Nagoya, walking the audience through the NPV sensitivity results across PV-penetration scenarios.
Attended the International Conference on Power and Energy Systems Engineering in Nagoya, presenting PV-economics research and exchanging ideas with energy researchers from around the world.
Networking with international researchers at CPESE 2018 — building collaborations around renewable-energy economics, PV integration, and power-system planning.
Volunteered as an instructor in the UI Mengajar programme — travelling to under-served schools to deliver energy-literacy lessons and encourage students to pursue science and engineering.
Invited as a guest lecturer at Polindra, sharing hands-on industry experience in power distribution, smart metering, and AI applications with engineering students.
The questions people ask most often when they are looking for an energy consultant or expert advisor on the energy transition, electric vehicles, or AI in the power sector.
Dr. Qashtalani Haramaini, S.T., M.T. is an Indonesian energy systems expert and researcher. He holds a Ph.D. in Electrical Engineering (Power Systems) from Universitas Indonesia and is completing a second Ph.D. at Monash University under the RACE for 2030 Cooperative Research Centre, researching Indonesia's electric-vehicle ecosystem. He is Energy Transaction Manager at PT PLN (Persero) with more than ten years in Indonesia's electricity sector, and the founder of EnVisor and Electra Academy.
He combines a doctorate in power systems with operational responsibility inside PT PLN (Persero), Indonesia's state electricity utility — so his research is validated on live distribution networks rather than in simulation only. His peer-reviewed work covers distributed solar PV siting and economics, net-metering policy for medium-voltage industrial consumers, Time-of-Use and dynamic tariffs, demand-side management, and load forecasting for tropical grids — the core policy levers of the energy transition. He has 10 peer-reviewed papers, 61 citations and an h-index of 4.
His current doctoral research at Monash University — Pathways to Advancing Sustainable Transportation: A Focus on Indonesia's EV Ecosystem — is carried out under the RACE for EVs program and supervised at Monash Art, Design and Architecture. It examines what Indonesia's charging infrastructure, electricity tariffs and urban form must become for electric vehicles to scale nationally.
He built a machine-learning electricity fraud-detection system at PT PLN that raised the field-inspection hit rate from roughly 15% to roughly 77%, recovering lost revenue on a non-smart-grid network — work that won 3rd place at the PLN National Innovation Awards 2024 and made him PLN West Java Innovation Champion 2025. Other applied AI work includes CERDAS, a complaint-prediction model; medium-term load forecasting with neural networks; a tamper-aware smart meter; and satellite night-lights plus GIS analysis of electrical infrastructure under the Esri Fellowship 2026. He also founded EnVisor, an AI-powered energy-efficiency advisor.
He is engaged as an energy consultant and expert advisor across six areas: energy transition strategy and policy; EV ecosystems and charging infrastructure; AI and data analytics for utilities; distribution performance and loss reduction; solar PV siting, economics and technical review; and executive training and capacity building. Clients include utilities and system operators, regulators and government bodies, renewable developers, investors, industrial energy users, and universities. See the full advisory page.
Yes. He works in English and Bahasa Indonesia, remotely or on site, and has an established international footprint: doctoral research at Monash University in Australia under the RACE for 2030 Cooperative Research Centre, an Australia Awards short course at Griffith University, IEEE professional membership, and conference presentations in Kuala Lumpur, Bali, Istanbul and Nagoya. He is based in Indramayu and Jakarta, Indonesia (UTC+7) — a time zone that overlaps the Asia-Pacific working day and the European morning.
Yes. He is open to research partnerships, AI collaborations and advisory or consulting engagements on Indonesia's energy transition, EV ecosystem and AI for utilities. He works in Bahasa Indonesia and English and is based in Indramayu and Jakarta, Indonesia. Reach him on LinkedIn or by email.
Open to research partnerships, AI collaborations, consulting conversations, and anything about Indonesia's energy future.