Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting

Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time series data often present intricate temporal variability and sharp fluctuations, which pose significant challenges for time series forecasting. Previous models that rely on 1D time series representations usually struggle with complex temporal variations. To address the limitations of 1D time series, this study introduces the Times2D method that transforms the 1D time series into 2D space. Times2D consists of three main parts: first, a Periodic Decomposition Block (PDB) that captures temporal variations within a period and between the same periods by converting the time series into a 2D tensor in the frequency domain. Second, the First and Second Derivative Heatmaps (FSDH) capture sharp changes and turning points, respectively. Finally, an Aggregation Forecasting Block (AFB) integrates the output tensors from PDB and FSDH for accurate forecasting. This 2D transformation enables the utilization of 2D convolutional operations to effectively capture long and short characteristics of the time series. Comprehensive experimental results across large-scale data in the literature demonstrate that the proposed Times2D model achieves state-of-the-art performance in both short-term and long-term forecasting. The code is available in this repository: https://github.com/Tims2D/Times2D.

SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting

Residential electricity demand forecasting is critical for efficient energy management and grid stability. Accurate predictions enable utility companies to optimize planning and operations. However, real-world residential electricity demand data often exhibit intricate temporal variability, including multiple seasonalities, periodicities, and abrupt fluctuations, which pose significant challenges for forecasting models. Previous models that rely on statistical methods, recurrent, convolutional neural networks, and transformers often struggle to capture these intricate temporal dynamics. To address these challenges, we propose the Seasonal-Periodic Decomposition Network (SPDNet), a novel deep learning framework consisting of two main modules. The first is the Seasonal-Trend Decomposition Module (STDM), which decomposes the input data into trend, seasonal, and residual components. The second is the Periodical Decomposition Module (PDM), which employs the Fast Fourier Transform to identify the dominant periods. For each dominant period, 1D input data is reshaped into a 2D tensor, where rows represent periods and columns correspond to frequencies. The 2D representations are then processed through three submodules: a 1D convolution to capture sharp fluctuations, a transformer-based encoder to model global patterns, and a 2D convolution to capture interactions between periods. Extensive experiments conducted on real-world residential electricity load data demonstrate that SPDNet outperforms traditional and advanced models in both forecasting accuracy and computational efficiency. The code is available in this repository: https://github.com/Tims2D/SPDNet.

Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy

Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. However, for the building occupancy status, direct sensing is costly, inaccurate, and intrusive for residents. So, forecasting algorithms could serve as an effective alternative. The goal of this study is to present a non-intrusive, accurate, and cost-effective approach, to develop a multi-objective simulation model for the application of DRPs in a smart residential house, where (a) electrical load demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints, and (c) , worst cases scenario approach is very conservative. Because that is unlikely all uncertain parameters take their worst values at all times. So, the flexible robust counterpart optimization along with uncertainty budgets is developed to consider uncertainty realistically. Simulated results indicate that considering uncertainty increases the costs by 36 percent and decreases the AC temperature setpoints. Besides, using DRPs reduces demand by shifting some appliance operations to off-peak hours and lowers costs by 13.2 percent.