International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Energy Consumption Forecasting and Visualization for Smart Homes Using Data Analytics and Machine Learning |
|---|---|
| ABSTRACT | Energy consumption forecasting is a critical component of smart home energy management systems, enabling efficient energy utilization and sustainable resource planning. This study presents a data-driven approach for forecasting electricity consumption using machine learning and deep learning techniques. A univariate time-series dataset comprising six years of hourly electricity consumption data is utilized for analysis. Data preprocessing techniques, including resampling, feature engineering, and normalization, are applied to enhance model performance. Two predictive models—Random Forest Regressor and Long Short-Term Memory (LSTM) network are implemented and evaluated. The dataset is divided into training and testing sets using an 80:20 ratio, and model performance is assessed using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that the LSTM model outperforms the Random Forest model by effectively capturing temporal dependencies and seasonal patterns in energy consumption data. The predicted results closely align with actual consumption trends, indicating high forecasting accuracy. The proposed approach can support smart grid systems, optimize energy distribution, and contribute to sustainable energy management in smart homes. |
| AUTHOR | G.NIRMALA, B. DEENAJA, B. TRILOK, B. BANDHAVI, B. PRAVEEN, B. RAMA JYOTHI Professor, Dept. of CSE, Sir CR Reddy College of Engineering, Eluru, India B. Tech Student, Dept. of CSE, Sir CR Reddy College of Engineering, Eluru, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404021 |
| pdf/21_Energy Consumption Forecasting and Visualization for Smart Homes Using Data Analytics and Machine Learning.pdf | |
| KEYWORDS | |
| References | [1] J. Brownlee, “Time Series Forecasting with Machine Learning,” 2019. Available: https://machinelearningmastery.com/time-series-forecasting-methods-in-python/ [2] A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow,” O’Reilly Media, 2019. [3] M. Nachawati, “Energy Consumption Prediction Using LSTM,” 2022. Available: https://www.theseus.fi/handle/10024/748970 [4] Fingrid, “Electricity Consumption Dataset,” Finland Transmission System Operator. Available: https://data.fingrid.fi/en/datasets/75 [5] F. Chollet, “Deep Learning with Python,” Manning Publications, 2018. [6] M. Qureshi et al., “Deep Learning-Based Forecasting of Electricity Consumption,” Scientific Reports, 2024. [7] C. V. S. Chandrika et al., “Advanced LSTM-Based Time Series Forecasting for Energy Consumption Management,” 2024. [8] F. Zhou et al., “Prediction of Building Energy Consumption Using RF-LSTM,” Energy Reports, 2024. [9] S. Ul Rehman et al., “Electricity Consumption Prediction in Smart Buildings Using Deep Learning Approaches,” 2025. [10] F. Liu et al., “Short-Term Multi-Energy Consumption Forecasting,” Scientific Reports, 2024. |
