The Wireless Body Area Networks (WBANs) are biosensors placed on the body, inside the body, and around it. Body Area Network (BAN) are designed in micro sizes and have limited resources. Biosensors sometimes have errors in data recording and faced with duplicate and noisy data in real time. Data redundancy causes a significant energy consumption in sending and receiving data in the sensor. One of the most effective ways to reduce data volume is to compress it to save more energy. To solve these problems, the Linear Predictive Run Length Coding method (LPRLC) is presented which is a combination of Linear Predictive Coding (LPC) for data prediction and Run Length Encoding (RLE) for data compression. The signals attained from biosensors include blood pressure systolic (BPsys), blood pressure diastolic (BPdias), Respiration, Oxygen, and Heart Rate, which are recorded as a time series. First, the received signal is predicted continuously, and then the error resulting from the actual signal and the expected signal is calculated. In the last step, the resulting error is compressed by the RLE algorithm and sent to the destination. To compare the criteria of Energy Conservation (EC) and Compression Rate (CR), Huffman, Arithmetic, and Lempel-Ziv-Welch (LZW) algorithms are placed instead of RLE. The results show that the RLE algorithm has an average of 98% energy saving and up to 70 times reduction of data volume compared to other algorithms, which has improved 6% in energy consumption and 9 times reduction of data volume.