Short term residential load forecasting using long short-term memory recurrent neural network

Abstract

<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.</span>

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

“…Exponential functions assign exponentially decreasing weights over time instead of giving equal weights to past observations, as in the case of ARIMA. It is used as a benchmark for comparison in recent smart-meter data studies [27,66].…”

“…It resembles the brain in the sense that knowledge is acquired by the network from its environment through a learning process and that interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge [67]. The long short-term memory (LSTM) approach used in this work is also largely found in other recent studies for commercial building load forecasting, photovoltaic power generation, and residential load forecasting [66,68,69].…”

Agriculture crop yield prediction using inertia based cat swarm optimization

“…Hence it is called a recurrent neural network, and it accomplishes the same function for every component. The function of RNN with time-series data [30] and the output of RNN obtain the best result which is utilized to determine the earlier information. The RNN combines the output and forgets the gate into a specific update gate 𝑢𝑝 𝑑 , in which the linear interpolation method is used to obtain a better result.…”

Optimization scheme for intelligent master controller with collaboratives energy system

“…A model-based long short-term memory (LSTM) using deep learning forecasting network for accurate and precise load forecast was developed by [25]. The authors made a comparison with two conventional models; exponential smoothing and autoregressive integrated moving average model, where LSTM was seen to outperform other models in its efficient response in memorizing large data sets.…”