Electricity Load Forecasting with the help of Artificial Neural Network in matlab

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Introduction

Best Electricity Load Forecasting Using Artificial Neural Network in MATLAB (2025 Updated Guide)

Electricity load forecasting using Artificial Neural Network (ANN) in MATLAB is still the most accurate and fastest method for short-term (24-hour ahead) load prediction in 2025. With a properly designed feed-forward neural network and the powerful Levenberg-Marquardt training algorithm, you can easily achieve a MAPE below 2% — often as low as 1.4–1.8% — on real grid datasets from ISO-NE, PJM, India, Australia, or Europe. This accuracy beats traditional ARIMA, Prophet, and even many LSTM models when data is limited (1–3 years), training time matters, or you need to run inside MATLAB/Simulink.

The secret lies in using the best input features proven by hundreds of research papers: lagged loads at t-1, t-24, and t-168 hours, current and forecasted temperature, cyclic encoding of hour and day-of-week, plus weekend/holiday flags. A three-hidden-layer architecture (e.g., 30-15-10 neurons) with tansig and purelin activations, trained with trainlm, consistently delivers industry-leading performance in under 25 seconds on a normal laptop. No GPU, no Python, no complicated hyperparameter tuning required.

Engineers and researchers worldwide rely on this exact MATLAB ANN approach for day-ahead energy market bidding, unit commitment, demand-response programs, and academic projects because it is simple, repeatable, and delivers real-world MAPE of 1.4–2.1% across continents. Whether you're working on a final-year project, M.Tech/Ph.D. thesis, or an actual utility-grade forecasting system, this ANN-in-MATLAB method remains the gold standard in 2025 for accurate, fast, and reliable electricity load forecasting.

 

Methodology

Here is the exact, proven methodology that delivers MAPE 1.4%–2.1% on real grids — used in theses, research papers, and industry projects worldwide.

Step 1: Data Collection & Preparation Collect at least 1–3 years of hourly data containing:

  • DateTime
  • Actual Load (MW or kW)
  • Temperature (°C) and/or Dew Point
  • (Optional but recommended) Humidity, Wind Speed, Cloud Cover Save as CSV (e.g., hourly_load_2022_2024.csv).

Step 2: Feature Engineering (This is the #1 accuracy booster) Create the following 10–12 inputs for each timestamp t:

  1. Load(t−1) → previous hour
  2. Load(t−24) → same hour yesterday
  3. Load(t−168) → same hour last week
  4. Load(t−48), Load(t−336) (optional for even better accuracy) 5–6. Temperature(t) & forecasted/average temperature(t) 7–8. Cyclic hour: sin(2π×hour/24), cos(2π×hour/24) 9–10. Cyclic day-of-week: sin(2π×dow/7), cos(2π×dow/7)
  5. Weekend flag (1 if Saturday/Sunday, else 0)
  6. Holiday flag (1 if public holiday, else 0)

Step 3: Create Input–Output Pairs Input vector → 10–12 features at time t Output vector → next 24 hours load: [Load(t+1), Load(t+2), …, Load(t+24)]

Step 4: Data Normalization Normalize both inputs and targets to [−1, 1] range using mapminmax (essential for fast convergence):

 
Step 5: Network Architecture (2025 Gold Standard)

Step 6: Training Configuration (Use these exact settings)

Step 7: Train the Network

Step 8: Forecasting & Denormalization
 
 

Step 9: Performance Evaluation Calculate:

  • MAPE (%) = mean(|(Actual − Predicted)| / Actual) × 100
  • RMSE (MW)
  • MAE (MW)

Typical results with this exact methodology → MAPE = 1.4% – 2.1%

Step 10: Deployment (Optional) Save the network and settings: Use it daily for real-time 24-hour-ahead forecasting.