# Building Energy Load Forecasting using Deep Neural Networks

arXiv: https://arxiv.org/abs/1610.09460 · HTML: https://mlsanity.com/paper/1610.09460/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2016.

## Built on by (most cited first, 24 of 24)

- [Machine Learning for a Sustainable Energy Future](https://mlsanity.com/paper/2210.10391/) (2022)
- [Federated Learning for Water Consumption Forecasting in Smart Cities](https://mlsanity.com/paper/2301.13036/) (2023)
- [Deep Learning for Energy Time-Series Analysis and Forecasting](https://mlsanity.com/paper/2306.09129/) (2023)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)
- [Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble](https://mlsanity.com/paper/2210.13108/) (2022)
- [Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM](https://mlsanity.com/paper/2302.05033/) (2023)
- [Leveraging Graph Neural Networks to Forecast Electricity Consumption](https://mlsanity.com/paper/2408.17366/) (2024)
- [Reinforcement Learning for Resilient Power Grids](https://mlsanity.com/paper/2212.04069/) (2022)
- [Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models](https://mlsanity.com/paper/2212.08757/) (2022)
- [Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting](https://mlsanity.com/paper/2304.03343/) (2023)
- [An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra](https://mlsanity.com/paper/2304.07832/) (2023)
- [Graph Neural Networks for Electricity Load Forecasting](https://mlsanity.com/paper/2507.03690/) (2025)
- [Day-Ahead Electricity Price Forecasting for Volatile Markets Using Foundation Models with Regularization Strategy](https://mlsanity.com/paper/2602.05430/) (2026)
- [A microservice-based framework for exploring data selection in cross-building knowledge transfer](https://mlsanity.com/paper/2102.12970/) (2021)
- [Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting](https://mlsanity.com/paper/2207.02589/) (2022)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction](https://mlsanity.com/paper/2302.10889/) (2023)
- [A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids](https://mlsanity.com/paper/2307.16149/) (2023)
- [Multi-variable Adversarial Time-Series Forecast Model](https://mlsanity.com/paper/2406.00596/) (2024)
- [LAD-BNet: Lag-Aware Dual-Branch Networks for Real-Time Energy Forecasting on Edge Devices](https://mlsanity.com/paper/2511.10680/) (2025)
- [Cyclical Temporal Encoding and Hybrid Deep Ensembles for Multistep Energy Forecasting](https://mlsanity.com/paper/2512.03656/) (2025)
- [A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems](https://mlsanity.com/paper/2603.25370/) (2026)
- [A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation](https://mlsanity.com/paper/2605.16250/) (2026)
- [Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems](https://mlsanity.com/paper/2609.19695/) (2026)

## Abstract (arXiv)

Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.