Impact of Time Series Forecasting Methods on Electricity Demand Prediction in a Smart Grid Notes: If you want more topics or adjustments (e.g., field, data, methods), tell me your preferences.

Project Abstract

This study evaluates the performance of contemporary time series forecasting methods for predicting electricity demand within a smart grid environment, focusing on accuracy, robustness, and computational efficiency across varying temporal horizons and load regimes. Leveraging a rich dataset comprising hourly electricity demand, weather variables (temperature, humidity, wind speed), calendar effects (holidays, day-of-week), and smart-meter granularity from an urban utility, the research compares classical approaches (ARIMA, SARIMA), state-of-the-art machine learning models (Gradient Boosting, Random Forest, XGBoost), and deep learning architectures (LSTM, Temporal Convolutional Networks) under a unified experimental framework. The study integrates feature engineering techniques such as differencing, lagged features, Fourier terms for seasonality, and exogenous variable incorporation to capture complex nonlinearities and regime shifts induced by aircraft traffic, industrial cycles, and demand response signals. Model evaluation uses a multi-metric protocol including mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coverage of prediction intervals, complemented by Diebold-Mariano tests to assess forecast accuracy differences over multiple horizons (h = 1 to 48 hours) and across seasonal periods (summer vs. winter). To ensure practical relevance, the research examines short-term (hour-ahead, 24-hour) and medium-term (48–168 hours) forecasting under scenarios with and without demand response participation, as well as resilience to missing data and data quality issues typical of smart grid telemetry. The study also conducts a computational efficiency assessment, comparing training times, inference latency, and scalability on edge-to-cloud architectures, with attention to model interpretability through feature importance analyses and SHAP values for tree-based models. A hybrid ensemble framework is proposed to balance accuracy and robustness, including stacking and weighted averaging guided by horizon-specific performance and uncertainty estimates derived from bootstrapping and probabilistic calibration techniques. Results are expected to reveal the relative strengths of linear versus nonlinear and probabilistic forecasting paradigms under different load patterns, highlighting conditions under which simpler models suffice and identifying contexts where advanced deep learning or ensemble approaches yield meaningful gains. The findings aim to inform grid operators on selecting appropriate forecasting strategies to enhance reliability, optimize generation scheduling, and improve demand-side management in smart grid ecosystems, while also providing actionable guidelines for data governance, model maintenance, and integration with existing energy management systems. The study contributes to the literature by delivering a comprehensive, horizon-aware comparison with rigorous statistical testing, transparent reporting of uncertainty, and pragmatic insights for real-world deployment in modern electricity networks.

Project Overview

What This Project Is About

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A straightforward study of how different time series forecasting methods can predict electricity demand in a smart grid. It compares simple and more advanced methods to see which gives more accurate and reliable forecasts for planning and operations.

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The Problem It Addresses

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Electricity systems rely on accurate demand forecasts to balance supply and reduce costs. Different forecasting methods can yield varying results, and there is a need to understand which methods work best in smart grid settings with real-time data and potential irregularities.

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Objectives of the Project

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1. Identify common time series forecasting methods used for electricity demand.
2. Evaluate forecast accuracy using real or simulated smart grid data.
3. Recommend methods that offer reliable performance for planning and operations.
4. Explain how data quality and features affect forecasts.
5. Provide guidance for practitioners on method selection.

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What You Will Do Step by Step

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1. Review basic literature on time series forecasting and smart grids.
2. Collect or simulate electricity demand data with typical patterns and anomalies.
3. Apply several forecasting methods (e.g., simple averages, ARIMA, and machine learning-inspired forecasts).
4. Assess forecast accuracy using metrics like error and reliability measures.
5. Analyze results, discuss practical implications, and prepare recommendations.

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Expected Outcome

A clear comparison of forecasting methods with practical guidance for selecting suitable approaches in smart grid contexts, plus insights on data quality and feature choices that influence accuracy.