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

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results

Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik

Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.   This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.