Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.

Electricity load forecasting with LSTM (Recurrent Neural Network)

使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vector machine, BP neural network, GRU, LSTM) are used for power system load forecasting / power forecasting.

Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-scrolling forecasting, and seq2seq forecasting.

Electric load forecast using Long-Short-Term-Memory (LSTM) recurrent neural network

Performed comparative analysis of BiLSTM, CNN-BiLSTM and CNN-BiLSTM with attention models for forecasting cases.

使用PYTorch框架建立的一个简单的LSTM模型来进行电力负荷预测

LSTM Model for Electric Load Forecasting

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this project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.

This repository implements the GAT-LSTM model, which combines Graph Attention Networks (GAT) and Long Short-Term Memory Networks (LSTM) for hourly power load forecasts. It leverages spatio-temporal dependencies in energy systems, using graph-structured data (e.g., grid topology) and temporal sequences (e.g., historical consumption and weather).

The work develops a multi-step time series load forecasting model that predicts daily power consumption for the upcoming week based on historic daily data of consumption at a university campus.

Load forecasting using LSTM and BP.使用LSTM、BP神经网络实现负荷预测

The Household Power Consumption dataset is a multivariate time series dataset which describes the electricity consumption over four years for a single household. They were tested to predict for a specific house and block of houses over a given period of time. Throughout the past couple of decades energy demand has increased exponentially. This increase loads the electricity distributors heavily. So forecasting future demand for electricity use would give the dealer an upper hand. Predicting the consumption of energy requires several parameters. This paper proposes two methods with one using a Recurrent Neural Network (RNN) and another using a Long Short Term Memory (LSTM) network, considering only the previous consumption of electricity to estimate potential consumption of electricity. To assess the applicability of the RNN and the LSTM network to predict the electricity consumption

1) How to Prepare Time Series Data for CNNs and LSTMs?? 2) How to Develop CNNs for Time Series Forecasting?? 3) How to Develop LSTMs for Time Series Forecasting?? 4) How to Load and Explore Household Energy Usage Data?? 5) How to Develop CNNs for Multi-step Energy Usage Forecasting?? 6) How to Develop LSTMs for Multi-step Energy Usage Forecasting

Contains the code for the paper "Multi-Horizon Short-Term Load Forecasting Using Hybrid of LSTM and Modified Split Convolution"

Renewable Energy Load Forecasting using LSTM-RNN

Develop a Time series forecasting model using LSTM deep learning model.15 month AMR data of Agriculture load in 30 minutes intervals have used to train the model and made the prediction for the next one month.

Short-Term-Residential-Load-forecasting

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Power load time series forecasting using multivariate deep LSTM for NAPS 2022 conference

Forecasted Electrical Load for Texas Power Station using LSTM

Load Forecasting using LSTM.

Deep Learning based hourly load forecasting of a residential zonal area in Mumbai using LSTM

Short-term Load Forecasting Based on LSTM

Implementation for Smart Microgrid Planning with Probabilistic Forecasting (CNN-LSTM+PSO), Multi-objective Sizing (NSGA-II with stochastic programming), and Energy Management using Deep Reinforcement Learning (DQN & PPO). Includes configs, reproducibility scripts, and synthetic load data.

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Univariate Time Series and LSTM

Keywords: Electricity prediction, Short-term load forecasting (STLF), Smart meters in London, Day-ahead load forecasting, electricity demand forecasting