Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Abstract

Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.

Smart Grids Data Analysis: A Systematic Mapping Study

“…], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], [90],…”

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

“…The LSTM has its unique trait of preserving information that previously passed through it by utilizing its hidden units [20,21]. In (6), ℎ represents the hidden state of the LSTM architecture. The Bi-LSTM model consists of two discrete LSTM networks where one access information in the forward direction and another access in the reverse direction [22].…”

“…The regression-based approach is one of the earliest and widely used statistical techniques. In the statistical methods such as holt-winter exponential smoothing (ES) [5] and auto-regressive integrated moving average [6], we identify the dominant variables based on the correlation analysis with the load for forecasting. As statistical approaches struggled to achieve efficient results with the highly non-linear load data, researchers gradually moved towards the machine learning (ML), artificial intelligence (AI), and a hybrid of statistical and AI-based learning models [7] for forecasting.…”

Dynamic Probabilistic Model Checking for Sensor Validation in Industry 4.0 Applications

“…Drift is typically a result of sensor wear and tear and calibration errors [7]. As the sensor's readings are in time series format, an ARIMA [13] model is used to calculate the trend component in order to determine whether there are consistent deviations (in increasing or decreasing order) in the sensor readings over time. The second module, the Rules Engine determines a sensor's confidence score based on the run-time sensor readings obtained from the pipeline by evaluating the degree of deviation from the sensor normal behaviour.…”