About
Dr. Madderla Chiranjeevi is an Assistant Professor in the School of Computer Science and Engineering at RV University, Bengaluru, India. He holds a Ph.D. degree from the National Institute of Technology Karnataka (NITK) Surathkal, where his research focused on solar irradiance forecasting using advanced hybrid deep learning models.
Dr. Madderla’s core research interests include renewable energy forecasting, machine learning, deep learning, hybrid AI architectures, time-series analysis, and intelligent energy systems. He has published several research articles in reputed journals and conferences on topics such as CNN-BiLSTM-Transformer models, attention mechanisms, feature engineering using XGBoost, error-correction techniques, and optimization algorithms for improving forecasting accuracy.
He has extensive experience in developing and implementing data-driven forecasting models for solar irradiance, solar power output, air quality index, and financial time series. His work integrates modern AI tools with real-world energy datasets to address challenges such as missing data, noise, outliers, and non-stationarity. In addition to research, Dr. Madderla is actively involved in teaching, curriculum development, and faculty development programs (FDPs). He has delivered technical sessions on artificial intelligence applications in forecasting, transformer architectures, and ML production systems.
Dr. Madderla is passionate about bridging the gap between academic research and industry practice, and he continuously mentors students and researchers in AI-driven energy analytics and applied data science.
Solar irradiation prediction hybrid framework using regularized convolutional BiLSTM-based autoencoder approach
Regularized Convolutional BiLSTM-Based Autoencoder Approach:
In this project, I have collected data from NSRDB, DKASC, and Hi-SEAS for performance evaluation.
Further, I designed hybrid technique which comprises two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM using Tensorflow/Keras using Python language.
Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO:
In this project, I developed and implemented K-means clustering technique to cluster the irradiance data.
The different clusters were predicted using CNN-BiLSTM-Attention model with PSO algorithm.
Solar Irradiance Forecasting using Hybrid Fuzzy based CNN-BiLSTM Framework:
Designed and implemented hybrid fuzzy based deep learning models (LSTM, CNN, CNN-LSTM, and CNN-BiLSTM)) using TensorFlow/Keras in Python to forecast solar irradiance.
Research Interests
Renewable energy forecasting, machine learning, deep learning, hybrid AI models, time-series analysis, intelligent energy systems, and data-driven modeling for power and sustainability applications.
Received the best paper award at IEEE-SPERT 2025 organized by SVNIT, Surat.
Served as a session chair at IEEE-SPET 2025 organized by SVNIT, Surat.
Dr. Madderla Chiranjeevi
Assistant Professor
Ph.DSchool of Computer Science and Engineering
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