A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

• Published in: JOIV : international journal on informatics visualization Online Vol. 7; no. 4
• Main Authors: Noaman, Salam Abdulkhaleq, Ahmed, Ali Mohammed Saleh, Salman, Aseel Dawod
• Format: Journal Article
• Language: English
• Published: 27.12.2023
• ISSN: 2549-9610; 2549-9904; 2549-9904
• DOI: 10.30630/joiv.7.4.1865

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level