The optimum use of energy carriers is one of the important factors affecting the sustainable growth and development of countries. Therefore, monitoring energy consumption is of considerable importance. Energy monitoring with Statistical Process Control (SPC) methods provides a breakdown of energy usage and makes it easier to perceive trends to reduce consumption. A review of the literature shows that previous research has addressed detecting change’s time in consumption. Developing these methods in spatial and temporal aspects of changes in energy consumption, which means detecting the time and location of changes simultaneously, would be able to provide more accurate diagnostic information. In this paper, a novel spatiotemporal framework based on the extension of the generalized likelihood ratio (GLR) and T2 control charts are used for monitoring the electricity consumption of eight-time series related to eight western cities in Mazandaran province, North of Iran, from March 21, 2019, to August 21, 2019. Due to the presence of autocorrelation in electricity consumption data, a model-based approach is proposed to reduce the autocorrelation’s effect on chart performance. The performance of the proposed charts in identifying significant deviations in electricity consumption was evaluated, which indicates the greater diagnostic power of the GLR chart in detecting the time and location of changes. Application of the recently-established spatiotemporal surveillance mechanisms for energy consumption monitoring is the main contribution of this study it would enable practitioners to analyze discrepancies of usage patterns better and make policies for continual improvement of the regional management of electricity distribution.
